Water quality management faces major challenges due to nonlinearity, data scarcity, spatial heterogeneity, and monitoring costs. Although numerous studies address machine learning (ML), there is a lack of comprehensive research on how ML practically overcomes these challenges. To fill this gap, this review categorizes ML capabilities into functional themes. Following PRISMA guidelines, this study synthesizes 86 peer-reviewed articles (2014–2026), prioritizing specific operational gains over general accuracy claims. For instance, random forest reduced RMSE by 60.1% compared to linear models, improving prediction accuracy. Moreover, ML models handle 50–70% missing data in time series reconstruction. To address data scarcity, transfer learning leverages knowledge from data-rich regions to enhance prediction efficiency (NSE) in data-poor regions by 7–17%. Unsupervised ML also excels at identifying hidden pollution sources through chemical fingerprinting, without prior labeling. Concurrently, feature ranking significantly lowers monitoring costs by reducing necessary variables from 13 to just 4, while maintaining robust prediction accuracy and enhancing model generalizability. Building upon these capabilities, integrating ML with IoT frameworks enables highly effective early-warning management, achieving up to 99.8% accuracy in real-time anomaly detection. Despite these advancements, the review carefully addresses technical risks such as overfitting and overestimation, emphasizing that continuous human oversight remains essential. The findings also revealed that no one-size model fits all scenarios, as predictive performance depends on data nature, sample size, model architecture, and environmental conditions. Ultimately, ML enables smart water-quality management, representing a qualitative shift toward proactive environmental resilience.
Ibrahim Said (Thu,) studied this question.