AI/ML water quality monitoring pipeline combines IoT sensor data with physics-based kinetic models and machine learning to predict contamination levels and classify water safety in real time. Raw sensor streams from fluorescence, chemical, and environmental sources flow through a medallion architecture—bronze ingestion, silver processing with noise filtering and normalization, and gold AI/ML fusion—where a hybrid model merges Random Forest regression and classification with kinetic analysis. The system outputs CFU/mL contamination estimates, water quality classifications, and confidence scores, then routes alerts through a real-time notification engine to water authorities and mobile endpoints. MLOps governance via MLflow and Unity Catalog ensures reproducible model training, registry management, and compliance across the entire pipeline.