Real-time water quality monitoring system combining fluorescence biosensors, heavy metal ISE sensors, and environmental probes with edge AI/ML inference. Raw sensor data flows from ESP32/Arduino microcontrollers through WiFi/IoT connectivity to a hybrid AI/ML pipeline that fuses physics-based kinetic modeling with Random Forest classification. The pipeline extracts kinetic features (lag time, reaction rate, ratiometric values), applies noise filtering and normalization, then outputs bacterial concentration, heavy metal levels, and water safety classification (Safe/Moderate/Unsafe) to a real-time dashboard with alert triggers. This architecture demonstrates how to embed domain knowledge (fluorescence kinetics) alongside machine learning for accurate, interpretable water quality predictions at the edge.