AI and Machine Learning Applications in Water Quality Monitoring and Treatment Optimization

The increasing complexity of water quality management and treatment systems is being driven by climate variability, rapid urbanization, and escalating regulatory demands, which collectively challenge conventional monitoring and control approaches. Recent advances in artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to address these challenges by enabling data-driven decision-making across water infrastructure systems. This study presents a critical synthesis of peer-reviewed literature on the application of AI and ML techniques for monitoring, prediction, and operational optimization in both drinking water and wastewater treatment systems. The reviewed evidence indicates that ML models significantly improve the prediction accuracy of key physicochemical and biological water quality parameters, including turbidity, nutrient concentrations, and microbial indicators. Additionally, AI-driven optimization frameworks demonstrate notable potential for enhancing operational efficiency, reducing energy consumption, and improving process stability, although their performance remains highly context-dependent. Despite these advancements, the effectiveness of AI-based solutions is constrained by persistent challenges related to data quality, limited model transferability across treatment systems, and the absence of robust post-deployment validation and assurance mechanisms. Overall, the findings highlight the need for standardized datasets, hybrid modeling approaches, and appropriate governance frameworks to support reliable, transparent, and scalable deployment of AI technologies in water treatment and management systems.