Advances in AI-Driven Surface Water Quality Assessment: A Comprehensive Review of Machine Learning and Emerging Technologies

This review synthesizes advances in machine learning (ML), deep learning, and complementary technologies for surface water quality assessment, drawing on peer-reviewed studies indexed in Scopus and ScienceDirect from 2015 to 2025. Using a PRISMA-guided protocol, we screened and analyzed articles addressing methodological developments, case studies, and operational deployments across rivers, lakes, reservoirs, groundwater-influenced systems, and urban/coastal waters. We summarize classical ML algorithms (regression, PCA, SVM, decision trees, random forests, gradient boosting), deep architectures (FNN, CNN, RNN/LSTM), and hybrid/ensemble strategies, and we evaluate common performance metrics (RMSE, MAE, R², NSE, ROC/AUC). In addition, we review complementary technologies as remote sensing and geospatial interpolation, IoT sensor networks with edge computing, big-data/cloud platforms, blockchain, and digital twins, and present representative case studies demonstrating improved spatio-temporal monitoring and predictive skill. Despite notable gains, persistent challenges include sparse and heterogeneous data, limited model interpretability, poor transferability across regions and scales, and barriers to regulatory acceptance. We therefore recommend priorities for research and practice: standardized data protocols and fusion methods, explainable and physics-informed models, transfer-learning frameworks, interoperable data infrastructures, and stakeholder co-development and capacity building to translate advanced analytics into routine water management. Collectively, these measures should improve resilience, inform policy, and safeguard aquatic ecosystems worldwide effectively.