Abstract
Rapid urbanization, industrial expansion, and population growth have placed increasing stress on urban water resources, making water quality management a critical challenge for sustainable smart cities. Surface water bodies, groundwater sources, and drinking water distribution networks in urban environments are continuously exposed to contamination from industrial effluents, domestic sewage, stormwater runoff, and aging infrastructure. Conventional water quality monitoring approaches, which rely primarily on periodic sampling and laboratory-based analysis, are insufficient to address these challenges due to limited temporal resolution, delayed reporting, and high operational costs. As a result, contamination events are often detected after significant degradation has already occurred, posing risks to public health and urban ecosystems. In recent years, smart city initiatives have emphasized the use of sensing technologies and digital platforms to improve urban infrastructure management. While sensor-based water quality monitoring systems have improved data availability, the effective interpretation of large-scale, heterogeneous, and time-varying water quality data remains a major challenge. Raw sensor data alone does not provide actionable intelligence unless combined with analytical and predictive capabilities that can support early warning, trend analysis, and informed decision-making. This paper presents a comprehensive AI-based water quality monitoring and prediction system designed specifically for sustainable smart city applications. The proposed framework integrates distributed water quality sensors with an intelligent data analytics layer to enable continuous monitoring, real-time assessment, and predictive forecasting of key physico-chemical water quality parameters. Machine learning models are employed to capture complex, non-linear relationships among water quality indicators and to predict future degradation trends and abnormal conditions. Emphasis is placed on system scalability, robustness, and interpretability to ensure practical deployment in real urban environments. By combining environmental engineering principles with data-driven intelligence, the proposed system supports proactive water quality management rather than reactive response. The framework enables municipal authorities and water utilities to identify emerging risks, optimize intervention strategies, and enhance long-term sustainability of urban water resources. This work contributes to the development of intelligent, resilient, and sustainable smart city water infrastructures.