diff --git a/docs/cookbook/index.md b/docs/cookbook/index.md
index da7eb9cb..81dd1e4f 100644
--- a/docs/cookbook/index.md
+++ b/docs/cookbook/index.md
@@ -83,6 +83,17 @@ Hands-on, production-ready examples for building healthcare AI applications with
   </div>
 </a>
 
+<a href="mlflow_experiment_tracking/" class="cookbook-card" data-tags="ml">
+  <div class="cookbook-card-icon">📊</div>
+  <div class="cookbook-card-title">Track Clinical NLP Experiments with MLflow</div>
+  <div class="cookbook-card-description">
+    Monitor healthcare AI model performance, log patient cohort metadata and regulatory context, generate FHIR Provenance for audit trails, and compare model versions in the MLflow UI.
+  </div>
+  <div class="cookbook-tags">
+    <span class="tag tag-ml">ML Research</span>
+  </div>
+</a>
+
 <a href="format_conversion/" class="cookbook-card" data-tags="healthtech interop fhir">
   <div class="cookbook-card-icon">🔄</div>
   <div class="cookbook-card-title">Convert Between Healthcare Data Formats</div>
diff --git a/docs/cookbook/mlflow_experiment_tracking.md b/docs/cookbook/mlflow_experiment_tracking.md
new file mode 100644
index 00000000..1dfafffd
--- /dev/null
+++ b/docs/cookbook/mlflow_experiment_tracking.md
@@ -0,0 +1,311 @@
+# Track Clinical NLP Experiments with MLflow
+
+Monitor and compare healthcare AI model performance using MLflow with HealthChain's healthcare-specific context and FHIR Provenance generation.
+
+## Why Track Healthcare ML Experiments?
+
+Healthcare AI requires more than standard ML metrics. You need to track:
+
+- **Patient cohort metadata** - What population was the model trained/evaluated on?
+- **Regulatory compliance** - HIPAA, FDA clearance status, audit requirements
+- **Data provenance** - Which EHR sources contributed to training data?
+- **Model lineage** - How does this version compare to production?
+
+HealthChain provides `HealthcareRunContext` to capture this metadata and automatically generate FHIR Provenance resources for interoperability with clinical systems.
+
+## Setup
+
+```bash
+pip install healthchain[mlflow]
+```
+
+Verify installation:
+
+```python
+from healthchain.mlflow import is_mlflow_available
+
+if is_mlflow_available():
+    print("MLflow ready!")
+```
+
+## Quick Start
+
+Use MLflow directly with HealthChain's healthcare context helper:
+
+```python
+import mlflow
+from healthchain.mlflow import HealthcareRunContext, log_healthcare_context
+from healthchain.pipeline import Pipeline
+from healthchain.pipeline.components.integrations import SpacyNLP
+from healthchain.io import Document
+
+# Build your NLP pipeline
+pipeline = Pipeline[Document]()
+pipeline.add_node(SpacyNLP.from_model_id("en_core_sci_sm"))
+nlp = pipeline.build()
+
+# Define healthcare context
+context = HealthcareRunContext(
+    model_id="clinical-ner-scispacy",
+    version="1.0.0",
+    organization="Hospital AI Lab",
+    purpose="Extract diagnoses from discharge summaries",
+    data_sources=["internal-ehr", "mimic-iv-notes"],
+    regulatory_tags=["HIPAA", "IRB-approved"],
+)
+
+# Track with MLflow
+mlflow.set_experiment("clinical-ner")
+
+with mlflow.start_run(run_name="scispacy-evaluation"):
+    # Log healthcare context (includes FHIR Provenance)
+    log_healthcare_context(context)
+
+    # Log model configuration
+    mlflow.log_params({
+        "spacy_model": "en_core_sci_sm",
+        "pipeline_version": "1.0.0",
+    })
+
+    # Run your evaluation
+    clinical_notes = [
+        "Patient presents with hypertension and type 2 diabetes.",
+        "Assessment: Community-acquired pneumonia.",
+    ]
+
+    total_entities = 0
+    for note in clinical_notes:
+        result = nlp(Document(note))
+        entities = result.nlp.get_entities()
+        total_entities += len(entities)
+
+    # Log metrics
+    mlflow.log_metrics({
+        "total_documents": len(clinical_notes),
+        "total_entities": total_entities,
+        "avg_entities_per_doc": total_entities / len(clinical_notes),
+    })
+```
+
+View results with `mlflow ui` at `http://localhost:5000`.
+
+## Healthcare Context
+
+### Patient Context
+
+Capture anonymized patient cohort information:
+
+```python
+from healthchain.mlflow import HealthcareRunContext, PatientContext
+
+patient_context = PatientContext(
+    cohort="General Medicine Inpatients",
+    age_range="18-90",
+    sample_size=500,
+    inclusion_criteria=[
+        "Admitted to general medicine ward",
+        "Has discharge summary",
+    ],
+    exclusion_criteria=[
+        "Psychiatric admission",
+        "Pediatric patients",
+    ],
+)
+
+context = HealthcareRunContext(
+    model_id="clinical-ner",
+    version="1.0.0",
+    patient_context=patient_context,
+    regulatory_tags=["HIPAA"],
+)
+```
+
+### FHIR Provenance
+
+HealthChain automatically generates a FHIR R4 Provenance resource for audit trails:
+
+```python
+# Generate FHIR Provenance manually if needed
+provenance = context.to_fhir_provenance()
+print(provenance.model_dump_json(indent=2))
+```
+
+??? example "Example FHIR Provenance Resource"
+
+    ```json
+    {
+      "resourceType": "Provenance",
+      "recorded": "2025-01-19T10:30:00Z",
+      "target": [{
+        "display": "Output of ML Model: clinical-ner-scispacy v1.0.0"
+      }],
+      "activity": {
+        "coding": [{
+          "system": "http://terminology.hl7.org/CodeSystem/v3-DataOperation",
+          "code": "DERIVE",
+          "display": "derive"
+        }],
+        "text": "Extract diagnoses from discharge summaries"
+      },
+      "agent": [{
+        "type": {
+          "coding": [{
+            "system": "http://terminology.hl7.org/CodeSystem/provenance-participant-type",
+            "code": "performer",
+            "display": "Performer"
+          }]
+        },
+        "who": {
+          "display": "ML Model: clinical-ner-scispacy v1.0.0"
+        }
+      }],
+      "policy": [
+        "urn:healthchain:datasource:internal-ehr",
+        "urn:healthchain:datasource:mimic-iv-notes",
+        "urn:healthchain:regulatory:HIPAA",
+        "urn:healthchain:regulatory:IRB-approved"
+      ]
+    }
+    ```
+
+## Comparing Model Versions
+
+Track multiple models in the same experiment:
+
+```python
+import mlflow
+from healthchain.mlflow import HealthcareRunContext, log_healthcare_context
+from healthchain.pipeline import Pipeline
+from healthchain.pipeline.components.integrations import SpacyNLP
+from healthchain.io import Document
+
+models_to_evaluate = [
+    "en_core_sci_sm",
+    "en_core_sci_md",
+    "en_core_sci_lg",
+]
+
+clinical_notes = [
+    "Patient is a 65-year-old male with hypertension and diabetes.",
+    "Assessment: Community-acquired pneumonia.",
+    "History of atrial fibrillation on anticoagulation.",
+]
+
+mlflow.set_experiment("clinical-ner-comparison")
+
+for model_name in models_to_evaluate:
+    # Build pipeline for this model
+    pipeline = Pipeline[Document]()
+    pipeline.add_node(SpacyNLP.from_model_id(model_name))
+    nlp = pipeline.build()
+
+    context = HealthcareRunContext(
+        model_id=f"clinical-ner-{model_name}",
+        version="1.0.0",
+    )
+
+    with mlflow.start_run(run_name=f"{model_name}-eval"):
+        log_healthcare_context(context)
+        mlflow.log_param("spacy_model", model_name)
+
+        total_entities = 0
+        for note in clinical_notes:
+            result = nlp(Document(note))
+            total_entities += len(result.nlp.get_entities())
+
+        mlflow.log_metrics({
+            "total_entities": total_entities,
+            "avg_entities_per_doc": total_entities / len(clinical_notes),
+        })
+```
+
+Use the MLflow UI to compare runs side-by-side.
+
+## Batch Processing
+
+For batch jobs, log step-wise metrics:
+
+```python
+import mlflow
+from healthchain.mlflow import HealthcareRunContext, log_healthcare_context
+
+mlflow.set_experiment("batch-processing")
+
+with mlflow.start_run(run_name="batch-evaluation"):
+    log_healthcare_context(HealthcareRunContext(
+        model_id="clinical-ner",
+        version="1.0.0",
+    ))
+
+    for i, note in enumerate(clinical_notes):
+        result = nlp(Document(note))
+        entities = result.nlp.get_entities()
+
+        # Log per-document metrics with step
+        mlflow.log_metrics({
+            "doc_entity_count": len(entities),
+            "doc_text_length": len(note),
+        }, step=i)
+
+    # Log final aggregate metrics
+    mlflow.log_metrics({
+        "total_documents": len(clinical_notes),
+    })
+```
+
+## Remote Tracking Server
+
+For team collaboration, use a remote MLflow server:
+
+```python
+import mlflow
+
+# Set tracking URI before any operations
+mlflow.set_tracking_uri("http://mlflow-server.example.com:5000")
+mlflow.set_experiment("clinical-nlp")
+
+with mlflow.start_run():
+    # ... your tracking code
+    pass
+```
+
+## API Reference
+
+### `log_healthcare_context(context, log_provenance=True)`
+
+Log healthcare context to the active MLflow run.
+
+**Parameters:**
+
+- `context` (HealthcareRunContext): Healthcare metadata to log
+- `log_provenance` (bool): Whether to log FHIR Provenance artifact. Default: True
+
+**Returns:** Dictionary of logged parameters
+
+**Raises:**
+
+- `ImportError`: If MLflow is not installed
+- `RuntimeError`: If no MLflow run is active
+
+### `HealthcareRunContext`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `model_id` | str | Unique identifier for the model (required) |
+| `version` | str | Model version string (required) |
+| `patient_context` | PatientContext | Anonymized patient cohort information |
+| `organization` | str | Organization responsible for the model |
+| `purpose` | str | Purpose or use case for the model |
+| `data_sources` | list[str] | Data sources used |
+| `regulatory_tags` | list[str] | Regulatory compliance tags |
+| `custom_metadata` | dict | Additional custom metadata |
+
+### `PatientContext`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `cohort` | str | Description of the patient cohort |
+| `age_range` | str | Age range (e.g., "18-65") |
+| `sample_size` | int | Number of patients |
+| `inclusion_criteria` | list[str] | Inclusion criteria |
+| `exclusion_criteria` | list[str] | Exclusion criteria |
diff --git a/healthchain/mlflow/__init__.py b/healthchain/mlflow/__init__.py
new file mode 100644
index 00000000..d36f48f2
--- /dev/null
+++ b/healthchain/mlflow/__init__.py
@@ -0,0 +1,169 @@
+"""MLflow integration for HealthChain experiment tracking.
+
+This module provides healthcare-specific context and FHIR Provenance generation
+for MLflow experiment tracking. It is designed to work with MLflow directly
+rather than wrapping it.
+
+Installation:
+    pip install healthchain[mlflow]
+
+Example:
+    >>> import mlflow
+    >>> from healthchain.mlflow import HealthcareRunContext, log_healthcare_context
+    >>>
+    >>> context = HealthcareRunContext(
+    ...     model_id="sepsis-predictor",
+    ...     version="1.0.0",
+    ...     purpose="Early sepsis detection",
+    ... )
+    >>>
+    >>> mlflow.set_experiment("clinical-nlp")
+    >>> with mlflow.start_run(run_name="evaluation-v1"):
+    ...     log_healthcare_context(context)
+    ...     # Your training/evaluation code here
+    ...     mlflow.log_metrics({"accuracy": 0.92})
+"""
+
+import logging
+from typing import Any, Dict
+
+logger = logging.getLogger(__name__)
+
+# Track whether mlflow is available
+_MLFLOW_AVAILABLE = False
+
+try:
+    import mlflow as _mlflow
+
+    _MLFLOW_AVAILABLE = True
+except ImportError:
+    _mlflow = None
+    logger.debug("MLflow not installed. Install with: pip install healthchain[mlflow]")
+
+
+def is_mlflow_available() -> bool:
+    """Check if MLflow is installed and available.
+
+    Returns:
+        True if MLflow is available, False otherwise
+    """
+    return _MLFLOW_AVAILABLE
+
+
+# Always import context classes (they don't require mlflow)
+from healthchain.mlflow.context import HealthcareRunContext, PatientContext  # noqa: E402
+
+
+def log_healthcare_context(
+    context: HealthcareRunContext,
+    log_provenance: bool = True,
+) -> Dict[str, Any]:
+    """Log healthcare context to the active MLflow run.
+
+    This function logs healthcare-specific metadata to MLflow, including
+    model information, patient cohort context, and optionally a FHIR
+    Provenance resource for audit trails.
+
+    If context.tracking_uri is set (populated automatically by
+    HealthcareRunContext.from_app_config()), the MLflow tracking URI is
+    configured before logging.
+
+    Args:
+        context: HealthcareRunContext with healthcare metadata
+        log_provenance: Whether to log FHIR Provenance as an artifact.
+            Defaults to True.
+
+    Returns:
+        Dictionary of logged parameters for reference
+
+    Raises:
+        ImportError: If MLflow is not installed
+        RuntimeError: If no MLflow run is active
+
+    Example:
+        >>> import mlflow
+        >>> from healthchain.config.appconfig import AppConfig
+        >>> from healthchain.mlflow import HealthcareRunContext, log_healthcare_context
+        >>>
+        >>> config = AppConfig.load()
+        >>> context = HealthcareRunContext.from_app_config(
+        ...     config,
+        ...     model_id="clinical-ner",
+        ...     purpose="Entity extraction from clinical notes",
+        ... )
+        >>>
+        >>> with mlflow.start_run():
+        ...     log_healthcare_context(context)
+    """
+    if not _MLFLOW_AVAILABLE:
+        raise ImportError(
+            "MLflow is not installed. Install with: pip install healthchain[mlflow]"
+        )
+
+    if context.tracking_uri is not None:
+        _mlflow.set_tracking_uri(context.tracking_uri)
+
+    # Check for active run
+    if _mlflow.active_run() is None:
+        raise RuntimeError(
+            "No active MLflow run. Call mlflow.start_run() first or use as context manager."
+        )
+
+    # Build parameters to log
+    params = {
+        "healthcare.model_id": context.model_id,
+        "healthcare.model_version": context.version,
+    }
+
+    if context.organization:
+        params["healthcare.organization"] = context.organization
+
+    if context.purpose:
+        params["healthcare.purpose"] = context.purpose
+
+    if context.patient_context:
+        params["healthcare.patient_cohort"] = context.patient_context.cohort
+        if context.patient_context.age_range:
+            params["healthcare.patient_age_range"] = context.patient_context.age_range
+        if context.patient_context.sample_size:
+            params["healthcare.patient_sample_size"] = (
+                context.patient_context.sample_size
+            )
+
+    if context.data_sources:
+        params["healthcare.data_sources"] = ", ".join(context.data_sources)
+
+    if context.regulatory_tags:
+        params["healthcare.regulatory_tags"] = ", ".join(context.regulatory_tags)
+
+    # Log parameters
+    _mlflow.log_params(params)
+
+    # Set healthcare-specific tags
+    _mlflow.set_tag("healthchain.model_id", context.model_id)
+    _mlflow.set_tag("healthchain.version", context.version)
+
+    # Log custom metadata as tags (avoids MLflow param value length limits)
+    for key, value in context.custom_metadata.items():
+        _mlflow.set_tag(f"healthcare.custom.{key}", str(value))
+
+    # Log FHIR Provenance as artifact if requested
+    if log_provenance:
+        provenance = context.to_fhir_provenance()
+        if provenance:
+            _mlflow.log_dict(
+                provenance.model_dump(exclude_none=True),
+                "fhir_provenance.json",
+            )
+            _mlflow.set_tag("healthchain.has_provenance", "true")
+            logger.info("Logged FHIR Provenance for healthcare audit trail")
+
+    return params
+
+
+__all__ = [
+    "HealthcareRunContext",
+    "PatientContext",
+    "log_healthcare_context",
+    "is_mlflow_available",
+]
diff --git a/healthchain/mlflow/context.py b/healthchain/mlflow/context.py
new file mode 100644
index 00000000..1ecaf8a9
--- /dev/null
+++ b/healthchain/mlflow/context.py
@@ -0,0 +1,315 @@
+"""Healthcare-specific run context with FHIR Provenance support."""
+
+import datetime
+import logging
+import uuid
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Field
+
+logger = logging.getLogger(__name__)
+
+
+class PatientContext(BaseModel):
+    """Anonymized patient context for healthcare tracking.
+
+    This model captures aggregate/anonymized patient information
+    suitable for logging in experiment tracking systems without
+    exposing PHI (Protected Health Information).
+
+    Attributes:
+        cohort: Description of the patient cohort (e.g., "ICU patients")
+        age_range: Age range of patients (e.g., "18-65")
+        sample_size: Number of patients in the cohort
+        inclusion_criteria: List of inclusion criteria for the cohort
+        exclusion_criteria: List of exclusion criteria for the cohort
+    """
+
+    cohort: str = Field(
+        default="unspecified",
+        description="Description of the patient cohort",
+    )
+    age_range: Optional[str] = Field(
+        default=None,
+        description="Age range of patients (e.g., '18-65')",
+    )
+    sample_size: Optional[int] = Field(
+        default=None,
+        description="Number of patients in the cohort",
+    )
+    inclusion_criteria: List[str] = Field(
+        default_factory=list,
+        description="List of inclusion criteria",
+    )
+    exclusion_criteria: List[str] = Field(
+        default_factory=list,
+        description="List of exclusion criteria",
+    )
+
+
+class HealthcareRunContext(BaseModel):
+    """Healthcare-specific run context with FHIR Provenance support.
+
+    This model captures healthcare metadata for ML experiment runs,
+    including model information, patient context, and regulatory
+    compliance data. It can generate FHIR Provenance resources
+    for audit trails required in healthcare settings.
+
+    Attributes:
+        model_id: Unique identifier for the model
+        version: Model version string
+        patient_context: Optional anonymized patient context
+        organization: Organization responsible for the model
+        purpose: Purpose or use case for the model
+        data_sources: List of data sources used
+        regulatory_tags: Regulatory compliance tags (e.g., "HIPAA", "FDA")
+        custom_metadata: Additional custom metadata
+
+    Example:
+        >>> context = HealthcareRunContext(
+        ...     model_id="sepsis-predictor",
+        ...     version="1.2.0",
+        ...     patient_context=PatientContext(
+        ...         cohort="ICU patients",
+        ...         age_range="18-90",
+        ...         sample_size=1000
+        ...     ),
+        ...     organization="Hospital AI Lab",
+        ...     purpose="Early sepsis detection"
+        ... )
+        >>> provenance = context.to_fhir_provenance()
+    """
+
+    model_id: str = Field(
+        ...,
+        description="Unique identifier for the model",
+    )
+    version: str = Field(
+        ...,
+        description="Model version string",
+    )
+    patient_context: Optional[PatientContext] = Field(
+        default=None,
+        description="Optional anonymized patient context",
+    )
+    organization: Optional[str] = Field(
+        default=None,
+        description="Organization responsible for the model",
+    )
+    purpose: Optional[str] = Field(
+        default=None,
+        description="Purpose or use case for the model",
+    )
+    data_sources: List[str] = Field(
+        default_factory=list,
+        description="List of data sources used",
+    )
+    regulatory_tags: List[str] = Field(
+        default_factory=list,
+        description="Regulatory compliance tags",
+    )
+    custom_metadata: Dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional custom metadata",
+    )
+    tracking_uri: Optional[str] = Field(
+        default=None,
+        description="MLflow tracking URI. Set automatically by from_app_config().",
+    )
+    recorded: Optional[datetime.datetime] = Field(
+        default=None,
+        description="Timestamp when the context was recorded",
+    )
+
+    def to_fhir_provenance(self) -> Optional[Any]:
+        """Generate a FHIR Provenance resource for audit trails.
+
+        Creates a FHIR R4 Provenance resource that documents the
+        ML model execution for healthcare regulatory compliance.
+
+        Returns:
+            FHIR Provenance resource if fhir.resources is available,
+            None otherwise
+
+        Note:
+            Requires fhir.resources package to be installed.
+        """
+        try:
+            from fhir.resources.codeableconcept import CodeableConcept
+            from fhir.resources.coding import Coding
+            from fhir.resources.provenance import Provenance, ProvenanceAgent
+            from fhir.resources.reference import Reference
+        except ImportError:
+            logger.warning(
+                "fhir.resources not available, cannot generate FHIR Provenance"
+            )
+            return None
+
+        # Use recorded time or current time
+        recorded_time = self.recorded or datetime.datetime.now(datetime.timezone.utc)
+
+        # Build agent for the ML model
+        agent = ProvenanceAgent(
+            type=CodeableConcept(
+                coding=[
+                    Coding(
+                        system="http://terminology.hl7.org/CodeSystem/provenance-participant-type",
+                        code="performer",
+                        display="Performer",
+                    )
+                ]
+            ),
+            who=Reference(
+                display=f"ML Model: {self.model_id} v{self.version}",
+            ),
+        )
+
+        # Build activity coding
+        activity = CodeableConcept(
+            coding=[
+                Coding(
+                    system="http://terminology.hl7.org/CodeSystem/v3-DataOperation",
+                    code="DERIVE",
+                    display="derive",
+                )
+            ],
+            text=self.purpose or "ML model inference",
+        )
+
+        # Build target reference (required field) - references what the provenance is about
+        target = [
+            Reference(
+                display=f"Output of ML Model: {self.model_id} v{self.version}",
+            )
+        ]
+
+        # Create Provenance resource with required fields
+        provenance = Provenance(
+            id=str(uuid.uuid4()),
+            target=target,
+            recorded=recorded_time.isoformat(),
+            activity=activity,
+            agent=[agent],
+        )
+
+        # Add policy references for data sources and regulatory tags
+        policies = []
+        if self.data_sources:
+            policies.extend(
+                [f"urn:healthchain:datasource:{ds}" for ds in self.data_sources]
+            )
+        if self.regulatory_tags:
+            policies.extend(
+                [f"urn:healthchain:regulatory:{tag}" for tag in self.regulatory_tags]
+            )
+        if policies:
+            provenance.policy = policies
+
+        return provenance
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert context to a dictionary for logging.
+
+        Returns:
+            Dictionary representation of the context
+        """
+        result = {
+            "model_id": self.model_id,
+            "version": self.version,
+            "organization": self.organization,
+            "purpose": self.purpose,
+            "data_sources": self.data_sources,
+            "regulatory_tags": self.regulatory_tags,
+        }
+
+        if self.patient_context:
+            result["patient_context"] = self.patient_context.model_dump()
+
+        if self.custom_metadata:
+            result["custom_metadata"] = self.custom_metadata
+
+        if self.recorded:
+            result["recorded"] = self.recorded.isoformat()
+
+        return result
+
+    @classmethod
+    def from_model_config(
+        cls,
+        model_id: str,
+        version: str,
+        **kwargs: Any,
+    ) -> "HealthcareRunContext":
+        """Create a HealthcareRunContext from model configuration.
+
+        Args:
+            model_id: Unique identifier for the model
+            version: Model version string
+            **kwargs: Additional context parameters
+
+        Returns:
+            HealthcareRunContext instance
+        """
+        return cls(
+            model_id=model_id,
+            version=version,
+            recorded=datetime.datetime.now(datetime.timezone.utc),
+            **kwargs,
+        )
+
+    @classmethod
+    def from_app_config(
+        cls,
+        config: Any,
+        model_id: str,
+        **kwargs: Any,
+    ) -> "HealthcareRunContext":
+        """Create a HealthcareRunContext populated from a HealthChain AppConfig.
+
+        Derives version, organization, data_sources, and regulatory_tags from
+        the AppConfig, reducing duplication between healthchain.yaml and
+        experiment tracking setup. Any field can be overridden via kwargs.
+
+        Args:
+            config: AppConfig instance loaded from healthchain.yaml
+            model_id: Unique identifier for the model (required, model-specific)
+            **kwargs: Override any HealthcareRunContext field
+
+        Returns:
+            HealthcareRunContext instance
+
+        Example:
+            >>> from healthchain.config.appconfig import AppConfig
+            >>> config = AppConfig.load()
+            >>> context = HealthcareRunContext.from_app_config(
+            ...     config,
+            ...     model_id="sepsis-predictor",
+            ...     purpose="Early sepsis detection",
+            ... )
+        """
+        derived: Dict[str, Any] = {
+            "version": config.version,
+            "tracking_uri": config.eval.tracking_uri,
+        }
+
+        if config.site.name:
+            derived["organization"] = config.site.name
+        elif config.name:
+            derived["organization"] = config.name
+
+        if config.sources:
+            derived["data_sources"] = list(config.sources.keys())
+
+        regulatory_tags: List[str] = []
+        if config.compliance.hipaa:
+            regulatory_tags.append("HIPAA")
+        if regulatory_tags:
+            derived["regulatory_tags"] = regulatory_tags
+
+        derived.update(kwargs)
+
+        return cls(
+            model_id=model_id,
+            recorded=datetime.datetime.now(datetime.timezone.utc),
+            **derived,
+        )
diff --git a/healthchain/pipeline/base.py b/healthchain/pipeline/base.py
index 24ed9b06..eabf4c3f 100644
--- a/healthchain/pipeline/base.py
+++ b/healthchain/pipeline/base.py
@@ -41,7 +41,7 @@ class ModelSource(Enum):
 
 @dataclass
 class ModelConfig:
-    """Configuration for model initialization"""
+    """Configuration for model initialization."""
 
     source: ModelSource
     model_id: Optional[str] = None
diff --git a/pyproject.toml b/pyproject.toml
index 7a18dacd..d2425f3f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,6 +56,11 @@ include = [
     "healthchain/configs/**/*.liquid",
 ]
 
+[project.optional-dependencies]
+mlflow = [
+    "mlflow>=2.14.0,<3",
+]
+
 [project.urls]
 Homepage = "https://dotimplement.github.io/HealthChain/"
 Repository = "https://github.com/dotimplement/HealthChain"
diff --git a/tests/mlflow/__init__.py b/tests/mlflow/__init__.py
new file mode 100644
index 00000000..8960e478
--- /dev/null
+++ b/tests/mlflow/__init__.py
@@ -0,0 +1 @@
+"""Tests for MLFlow integration module."""
diff --git a/tests/mlflow/conftest.py b/tests/mlflow/conftest.py
new file mode 100644
index 00000000..9673a747
--- /dev/null
+++ b/tests/mlflow/conftest.py
@@ -0,0 +1,33 @@
+"""Fixtures for MLflow integration tests."""
+
+import pytest
+
+
+@pytest.fixture
+def patient_context():
+    """Provides a PatientContext for testing."""
+    from healthchain.mlflow.context import PatientContext
+
+    return PatientContext(
+        cohort="ICU patients",
+        age_range="18-65",
+        sample_size=1000,
+        inclusion_criteria=["admitted to ICU", "age >= 18"],
+        exclusion_criteria=["DNR orders"],
+    )
+
+
+@pytest.fixture
+def healthcare_context(patient_context):
+    """Provides a HealthcareRunContext for testing."""
+    from healthchain.mlflow.context import HealthcareRunContext
+
+    return HealthcareRunContext(
+        model_id="sepsis-predictor",
+        version="1.2.0",
+        patient_context=patient_context,
+        organization="Test Hospital",
+        purpose="Early sepsis detection",
+        data_sources=["MIMIC-IV", "internal-ehr"],
+        regulatory_tags=["HIPAA", "FDA-cleared"],
+    )
diff --git a/tests/mlflow/test_context.py b/tests/mlflow/test_context.py
new file mode 100644
index 00000000..066ac25c
--- /dev/null
+++ b/tests/mlflow/test_context.py
@@ -0,0 +1,238 @@
+"""Tests for PatientContext and HealthcareRunContext."""
+
+import datetime
+import pytest
+from pydantic import ValidationError
+
+from healthchain.mlflow.context import PatientContext, HealthcareRunContext
+
+
+# PatientContext tests
+
+
+def test_patient_context_default_values():
+    """Test PatientContext with default values."""
+    context = PatientContext()
+
+    assert context.cohort == "unspecified"
+    assert context.age_range is None
+    assert context.sample_size is None
+    assert context.inclusion_criteria == []
+    assert context.exclusion_criteria == []
+
+
+def test_patient_context_custom_values():
+    """Test PatientContext with custom values."""
+    context = PatientContext(
+        cohort="ICU patients",
+        age_range="18-65",
+        sample_size=500,
+        inclusion_criteria=["admitted to ICU", "mechanically ventilated"],
+        exclusion_criteria=["DNR orders", "palliative care"],
+    )
+
+    assert context.cohort == "ICU patients"
+    assert context.age_range == "18-65"
+    assert context.sample_size == 500
+    assert context.inclusion_criteria == ["admitted to ICU", "mechanically ventilated"]
+    assert context.exclusion_criteria == ["DNR orders", "palliative care"]
+
+
+def test_patient_context_model_dump():
+    """Test PatientContext serialization."""
+    context = PatientContext(
+        cohort="test-cohort",
+        age_range="30-50",
+        sample_size=100,
+    )
+
+    data = context.model_dump()
+
+    assert data["cohort"] == "test-cohort"
+    assert data["age_range"] == "30-50"
+    assert data["sample_size"] == 100
+
+
+# HealthcareRunContext tests
+
+
+def test_healthcare_context_required_fields():
+    """Test that model_id and version are required."""
+    with pytest.raises(ValidationError):
+        HealthcareRunContext()
+
+    with pytest.raises(ValidationError):
+        HealthcareRunContext(model_id="test")
+
+    with pytest.raises(ValidationError):
+        HealthcareRunContext(version="1.0")
+
+
+def test_healthcare_context_minimal():
+    """Test HealthcareRunContext with minimal required fields."""
+    context = HealthcareRunContext(model_id="my-model", version="1.0.0")
+
+    assert context.model_id == "my-model"
+    assert context.version == "1.0.0"
+    assert context.patient_context is None
+    assert context.organization is None
+    assert context.purpose is None
+    assert context.data_sources == []
+    assert context.regulatory_tags == []
+    assert context.custom_metadata == {}
+
+
+def test_healthcare_context_full(patient_context):
+    """Test HealthcareRunContext with all fields."""
+    context = HealthcareRunContext(
+        model_id="sepsis-predictor",
+        version="2.1.0",
+        patient_context=patient_context,
+        organization="Test Hospital",
+        purpose="Early sepsis detection",
+        data_sources=["MIMIC-IV", "eICU"],
+        regulatory_tags=["HIPAA", "FDA"],
+        custom_metadata={"validated": True, "threshold": 0.7},
+    )
+
+    assert context.model_id == "sepsis-predictor"
+    assert context.version == "2.1.0"
+    assert context.patient_context.cohort == "ICU patients"
+    assert context.organization == "Test Hospital"
+    assert context.purpose == "Early sepsis detection"
+    assert context.data_sources == ["MIMIC-IV", "eICU"]
+    assert context.regulatory_tags == ["HIPAA", "FDA"]
+    assert context.custom_metadata == {"validated": True, "threshold": 0.7}
+
+
+def test_healthcare_context_to_dict_minimal():
+    """Test to_dict with minimal context."""
+    context = HealthcareRunContext(model_id="test-model", version="1.0")
+
+    result = context.to_dict()
+
+    assert result["model_id"] == "test-model"
+    assert result["version"] == "1.0"
+    assert result["organization"] is None
+    assert result["purpose"] is None
+    assert result["data_sources"] == []
+    assert result["regulatory_tags"] == []
+    assert "patient_context" not in result
+    assert "custom_metadata" not in result
+
+
+def test_healthcare_context_to_dict_full(patient_context):
+    """Test to_dict with full context."""
+    recorded_time = datetime.datetime(
+        2024, 1, 15, 10, 30, 0, tzinfo=datetime.timezone.utc
+    )
+    context = HealthcareRunContext(
+        model_id="test-model",
+        version="2.0",
+        patient_context=patient_context,
+        organization="Hospital",
+        purpose="Testing",
+        data_sources=["source1"],
+        regulatory_tags=["HIPAA"],
+        custom_metadata={"key": "value"},
+        recorded=recorded_time,
+    )
+
+    result = context.to_dict()
+
+    assert result["model_id"] == "test-model"
+    assert result["version"] == "2.0"
+    assert result["organization"] == "Hospital"
+    assert result["purpose"] == "Testing"
+    assert result["data_sources"] == ["source1"]
+    assert result["regulatory_tags"] == ["HIPAA"]
+    assert "patient_context" in result
+    assert result["patient_context"]["cohort"] == "ICU patients"
+    assert "custom_metadata" in result
+    assert result["custom_metadata"]["key"] == "value"
+    assert "recorded" in result
+
+
+def test_healthcare_context_from_model_config():
+    """Test from_model_config factory method."""
+    context = HealthcareRunContext.from_model_config(
+        model_id="factory-model",
+        version="3.0",
+        organization="Factory Org",
+        purpose="Factory test",
+    )
+
+    assert context.model_id == "factory-model"
+    assert context.version == "3.0"
+    assert context.organization == "Factory Org"
+    assert context.purpose == "Factory test"
+    assert context.recorded is not None
+
+
+def test_healthcare_context_to_fhir_provenance_minimal():
+    """Test to_fhir_provenance with minimal context."""
+    context = HealthcareRunContext(
+        model_id="test-model",
+        version="1.0",
+        purpose="Testing provenance generation",
+    )
+
+    provenance = context.to_fhir_provenance()
+
+    assert provenance is not None
+    assert provenance.__class__.__name__ == "Provenance"
+    assert len(provenance.target) == 1
+    assert "test-model" in provenance.target[0].display
+    assert len(provenance.agent) == 1
+    assert provenance.activity.text == "Testing provenance generation"
+    assert provenance.policy is None  # No data sources or regulatory tags
+
+
+def test_healthcare_context_to_fhir_provenance_with_regulatory_tags():
+    """Test to_fhir_provenance includes regulatory tags in policy field."""
+    context = HealthcareRunContext(
+        model_id="regulated-model",
+        version="1.0",
+        regulatory_tags=["HIPAA", "FDA-cleared"],
+    )
+
+    provenance = context.to_fhir_provenance()
+
+    assert provenance is not None
+    assert provenance.policy is not None
+    assert len(provenance.policy) == 2
+    assert "urn:healthchain:regulatory:HIPAA" in provenance.policy
+    assert "urn:healthchain:regulatory:FDA-cleared" in provenance.policy
+
+
+def test_healthcare_context_to_fhir_provenance_with_data_sources():
+    """Test to_fhir_provenance includes data sources in policy field."""
+    context = HealthcareRunContext(
+        model_id="test-model",
+        version="1.0",
+        data_sources=["mimic-iv", "internal-ehr"],
+    )
+
+    provenance = context.to_fhir_provenance()
+
+    assert provenance is not None
+    assert provenance.policy is not None
+    assert len(provenance.policy) == 2
+    assert "urn:healthchain:datasource:mimic-iv" in provenance.policy
+
+
+def test_healthcare_context_to_fhir_provenance_full():
+    """Test to_fhir_provenance with all fields."""
+    context = HealthcareRunContext(
+        model_id="full-model",
+        version="2.0",
+        data_sources=["source1"],
+        regulatory_tags=["HIPAA"],
+    )
+
+    provenance = context.to_fhir_provenance()
+
+    assert provenance is not None
+    assert len(provenance.policy) == 2
+    assert "urn:healthchain:datasource:source1" in provenance.policy
+    assert "urn:healthchain:regulatory:HIPAA" in provenance.policy
diff --git a/tests/mlflow/test_init.py b/tests/mlflow/test_init.py
new file mode 100644
index 00000000..00892a63
--- /dev/null
+++ b/tests/mlflow/test_init.py
@@ -0,0 +1,124 @@
+"""Tests for MLflow module imports and availability checking."""
+
+import pytest
+import tempfile
+
+
+def test_is_mlflow_available_returns_bool():
+    """Test is_mlflow_available returns boolean."""
+    from healthchain.mlflow import is_mlflow_available
+
+    result = is_mlflow_available()
+
+    assert isinstance(result, bool)
+
+
+def test_patient_context_always_importable():
+    """Test PatientContext can always be imported."""
+    from healthchain.mlflow import PatientContext
+
+    context = PatientContext()
+    assert context.cohort == "unspecified"
+
+
+def test_healthcare_run_context_always_importable():
+    """Test HealthcareRunContext can always be imported."""
+    from healthchain.mlflow import HealthcareRunContext
+
+    context = HealthcareRunContext(model_id="test", version="1.0")
+    assert context.model_id == "test"
+
+
+def test_all_exports_defined():
+    """Test that __all__ contains expected exports."""
+    import healthchain.mlflow as mlflow_module
+
+    expected_exports = [
+        "HealthcareRunContext",
+        "PatientContext",
+        "log_healthcare_context",
+        "is_mlflow_available",
+    ]
+
+    for export in expected_exports:
+        assert export in mlflow_module.__all__
+
+
+def test_log_healthcare_context_importable():
+    """Test log_healthcare_context can be imported."""
+    from healthchain.mlflow import log_healthcare_context
+
+    assert callable(log_healthcare_context)
+
+
+def test_log_healthcare_context_requires_active_run():
+    """Test log_healthcare_context raises error without active run."""
+    pytest.importorskip("mlflow")
+
+    from healthchain.mlflow import HealthcareRunContext, log_healthcare_context
+
+    context = HealthcareRunContext(model_id="test", version="1.0")
+
+    with pytest.raises(RuntimeError, match="No active MLflow run"):
+        log_healthcare_context(context)
+
+
+def test_log_healthcare_context_success():
+    """Test log_healthcare_context logs params and tags to an active MLflow run."""
+    mlflow = pytest.importorskip("mlflow")
+
+    from healthchain.mlflow import (
+        HealthcareRunContext,
+        PatientContext,
+        log_healthcare_context,
+    )
+
+    context = HealthcareRunContext(
+        model_id="test-model",
+        version="1.0.0",
+        organization="Test Org",
+        purpose="Unit testing",
+        data_sources=["mimic-iv"],
+        regulatory_tags=["HIPAA"],
+        patient_context=PatientContext(cohort="ICU patients", sample_size=500),
+        custom_metadata={"threshold": 0.7, "validated": True},
+    )
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        mlflow.set_tracking_uri(f"file://{tmp_dir}")
+        mlflow.set_experiment("test-experiment")
+        with mlflow.start_run():
+            logged = log_healthcare_context(context, log_provenance=False)
+
+            run = mlflow.active_run()
+            run_data = mlflow.get_run(run.info.run_id).data
+
+    assert logged["healthcare.model_id"] == "test-model"
+    assert logged["healthcare.model_version"] == "1.0.0"
+    assert logged["healthcare.organization"] == "Test Org"
+    assert logged["healthcare.regulatory_tags"] == "HIPAA"
+    assert logged["healthcare.data_sources"] == "mimic-iv"
+    assert logged["healthcare.patient_cohort"] == "ICU patients"
+    assert run_data.params["healthcare.model_id"] == "test-model"
+    assert run_data.tags["healthchain.model_id"] == "test-model"
+    assert run_data.tags["healthcare.custom.threshold"] == "0.7"
+    assert run_data.tags["healthcare.custom.validated"] == "True"
+
+
+def test_context_models_independent_of_mlflow():
+    """Test context models work without mlflow installed."""
+    from healthchain.mlflow.context import HealthcareRunContext, PatientContext
+
+    patient = PatientContext(cohort="test-cohort", sample_size=100)
+    assert patient.sample_size == 100
+
+    context = HealthcareRunContext(
+        model_id="test-model",
+        version="1.0",
+        patient_context=patient,
+    )
+    assert context.patient_context.cohort == "test-cohort"
+
+    # to_dict should work
+    data = context.to_dict()
+    assert data["model_id"] == "test-model"
diff --git a/uv.lock b/uv.lock
index 71bfce78..cb98a2ae 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,9 +2,27 @@ version = 1
 revision = 3
 requires-python = ">=3.10, <3.14"
 resolution-markers = [
-    "python_full_version >= '3.12'",
-    "python_full_version == '3.11.*'",
-    "python_full_version < '3.11'",
+    "python_full_version >= '3.12' and sys_platform != 'win32'",
+    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version < '3.11' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform == 'win32'",
+]
+
+[[package]]
+name = "alembic"
+version = "1.18.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mako" },
+    { name = "sqlalchemy" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/94/13/8b084e0f2efb0275a1d534838844926f798bd766566b1375174e2448cd31/alembic-1.18.4.tar.gz", hash = "sha256:cb6e1fd84b6174ab8dbb2329f86d631ba9559dd78df550b57804d607672cedbc", size = 2056725, upload-time = "2026-02-10T16:00:47.195Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/29/6533c317b74f707ea28f8d633734dbda2119bbadfc61b2f3640ba835d0f7/alembic-1.18.4-py3-none-any.whl", hash = "sha256:a5ed4adcf6d8a4cb575f3d759f071b03cd6e5c7618eb796cb52497be25bfe19a", size = 263893, upload-time = "2026-02-10T16:00:49.997Z" },
 ]
 
 [[package]]
@@ -79,6 +97,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/02/e3/a4fa1946722c4c7b063cc25043a12d9ce9b4323777f89643be74cef2993c/backrefs-6.1-py39-none-any.whl", hash = "sha256:a9e99b8a4867852cad177a6430e31b0f6e495d65f8c6c134b68c14c3c95bf4b0", size = 381058, upload-time = "2025-11-15T14:52:06.698Z" },
 ]
 
+[[package]]
+name = "blinker"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460, upload-time = "2024-11-08T17:25:47.436Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458, upload-time = "2024-11-08T17:25:46.184Z" },
+]
+
 [[package]]
 name = "blis"
 version = "1.3.3"
@@ -118,6 +145,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/31/07/4cdc81a47bf862c0b06d91f1bc6782064e8b69ac9b5d4ff51d97e4ff03da/blis-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:7a0fc4b237a3a453bdc3c7ab48d91439fcd2d013b665c46948d9eaf9c3e45a97", size = 6192624, upload-time = "2025-11-17T12:28:14.197Z" },
 ]
 
+[[package]]
+name = "cachetools"
+version = "5.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/81/3747dad6b14fa2cf53fcf10548cf5aea6913e96fab41a3c198676f8948a5/cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4", size = 28380, upload-time = "2025-02-20T21:01:19.524Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/72/76/20fa66124dbe6be5cafeb312ece67de6b61dd91a0247d1ea13db4ebb33c2/cachetools-5.5.2-py3-none-any.whl", hash = "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a", size = 10080, upload-time = "2025-02-20T21:01:16.647Z" },
+]
+
 [[package]]
 name = "catalogue"
 version = "2.0.10"
@@ -302,6 +338,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ae/8a/c4bb04426d608be4a3171efa2e233d2c59a5c8937850c10d098e126df18e/cloudpathlib-0.23.0-py3-none-any.whl", hash = "sha256:8520b3b01468fee77de37ab5d50b1b524ea6b4a8731c35d1b7407ac0cd716002", size = 62755, upload-time = "2025-10-07T22:47:54.905Z" },
 ]
 
+[[package]]
+name = "cloudpickle"
+version = "3.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330, upload-time = "2025-11-03T09:25:26.604Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228, upload-time = "2025-11-03T09:25:25.534Z" },
+]
+
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