Automated Machine Learning Explainability Pipeline is a production-ready framework that makes model decisions transparent, auditable, and compliant. It couples model-agnostic explainers with automation and observability so every run produces consistent, business-friendly evidence for why predictions were made. The system supports both global and local explainability: SHAP (Tree/Kernel) and LIME quantify overall feature influence and generate per-instance rationales, enabling bias checks, validation, and stakeholder trust. A typed data model (ExplainabilityReport) standardizes outputs—timestamps, model version, global importance, local attributions—and serializes them to JSON. Visual artifacts (SHAP summary bar plots) and LIME HTML explanations are created alongside an LLM-written executive summary (Gemini) that translates technical signals into plain language. All artifacts are logged to MLflow for lineage, comparison across versions, and long-term monitoring.