<p>Climate change is gradually affecting the quality, reliability and safety of potable water throughout the world. Rising temperatures, excess rainfall and changing hydrological cycles are altering the physical, chemical and biological characteristics of water sources,. This in turn poses serious challenges to drinking water facilities and water distribution systems. Artificial intelligence (AI), machine learning (ML) and internet of things (IoT) have emerged as powerful tools for better water quality (WQ) monitoring, optimisation of treatment and management of infrastructure. This review is a critical assessment of the recent developments of AI-based approaches for the evaluation and remediation of drinking water quality under changing climatic conditions. It involves management of chlorination, adsorption, membrane filtration and the risk foreseen for the water distribution network in a city. The review addresses a critical gap by synthesising an integrated AI‑driven framework to mitigate climate‑induced WQ variability and support resilient potable water systems. It also examines the integration of AI with IoT-enabled sensor systems for real-time monitoring and predictive risk management. Furthermore, the review discusses current limitations, operational challenges, and sustainability implications of AI-based water management systems. The outcome demonstrates a growing potential of data-driven technologies to facilitate adaptive decision-making, enhance the efficiency of water treatment systems. It also portrays the resiliency of drinking water systems under climate change pressures.</p> Graphical Abstract <p></p>