Contamination events in drinking water distribution systems pose acute public health risks. Early detection is critical—typical contamination (chemical, microbial, or physical) travels through distribution networks, potentially affecting thousands within hours. We present a real-time anomaly detection system using multivariate sensor fusion and Isolation Forest algorithms. The system monitors six water quality parameters simultaneously (pH, turbidity, free chlorine, dissolved oxygen, electrical conductivity, temperature) at normal ranges specified by EPA Safe Drinking Water Act regulations. We evaluate three machine learning approaches: Isolation Forest, Local Outlier Factor (LOF), and multivariate Gaussian detection, on synthetic water quality data spanning 30 days with injected contamination events. Isolation Forest achieves 90.4% F1-score and 89.2% recall with <6 hour mean detection latency. The approach is computationally efficient, operational without internet connectivity, and provides explainable anomalies through feature attribution. Field validation on real distribution systems and integration with SCADA alert systems could enable autonomous contamination response, protecting public health and water infrastructure.