<p>This paper presents an innovative real-time water monitoring system designed to ensure the quality of water from a single source till household distribution. The system employs advanced sensors to measure Turbidity, Pressure, pH, and Total Dissolved Solids (TDS) at multiple stages, the source filtration tank, the primary filtration tank, and the household tanks. Data from these sensors are transmitted to a Command Center (CC) for storage and analysis using a Machine Learning (ML) model. The integration with ML models facilitates real-time data analysis, predictive maintenance, and anomaly detection. A robust alert mechanism ensures timely interventions by notifying technicians when water quality parameters fall below acceptable thresholds. The proposed system optimizes water quality across the distribution network but also enhances the overall safety and reliability of water supply. Experimental results demonstrate the system’s efficacy, with models like Gradient Boosting Machine (GBM) and Random Forest (RF) achieving high accuracy and reliability in water quality classification.</p>