ABSTRACT Water quality deterioration has intensified the need for rapid and accurate assessment using modern monitoring approaches. Conventional laboratory‐based techniques often suffer from delayed analysis and low sampling frequency, limiting timely decision‐making. Key quantitative parameters—including pH (optimal 6.5–8.5), dissolved oxygen (DO > 5 mg/L for aquatic health), turbidity ( 100 reflecting poor conditions)—serve as essential indicators of ecosystem and human health. Recent advancements in Internet of Things (IoT) sensors, artificial intelligence (AI), and machine learning (ML) have enabled high‐frequency measurements, predictive forecasting, anomaly detection, and enhanced early warning capabilities. IoT‐enabled multiparameter sensing combined with ML models such as random forests, gradient boosting, and deep neural networks significantly improve accuracy in pollutant prediction and trend analysis. This review synthesizes the latest progress in IoT‐, AI‐, and ML‐driven water quality monitoring, outlines quantitative improvements reported in recent literature, and highlights remaining technical, economic, and governance challenges influencing large‐scale deployment.
Dutta et al. (2026) studied this question.