Access to safe drinking water is a critical global challenge, with millions exposed to chemical and microbial hazards. For example, in Bangladesh, widespread arsenic (As) contamination of groundwater threatens public health, while in the United States of America (U.S.), aging infrastructure and emerging pollutants amplify disparities in water safety. Traditional approaches often fail to address the interconnected and dynamic nature of these challenges, particularly in the face of climate change and co-exposure effects. This chapter explores how artificial intelligence (AI) can enhance drinking water safety by integrating diverse datasets, including environmental, health, socio-economic, and geospatial information. It presents AI-driven approaches for mapping contamination hotspots, predicting emerging risks, and optimizing interventions. Case studies from Bangladesh and the United States illustrate AI’s role in advancing data-driven risk-assessment and targeted mitigation strategies. The literature review provided in this chapter highlights AI’s ability to identify contamination sources, model complex co-exposures, and enhance real-time monitoring. By applying machine learning techniques, AI refines arsenic risk assessment in groundwater, detects contamination trends, and optimizes early warning systems for vulnerable communities. These applications demonstrate how AI shifts water safety management from reactive responses to proactive, data-driven decision-making. Artificial intelligence represents a transformative tool in managing drinking water safety, offering predictive capabilities and integrative approaches that surpass traditional methods. By fostering interdisciplinary collaboration and improving contamination monitoring, AI has the potential to address global water safety challenges while promoting equity and sustainability across diverse socio-economic and environmental contexts.