Assessing and ensuring safe drinking water supply is a global challenge with conventional approaches. ML as a novel tool is promising in monitoring and protecting drinking water quality, especially in municipal engineered water systems. This review for the first time comprehensively summarizes the applications of ML in assessing and ensuring safe drinking water supply with a focus on water quality in engineered water systems. We compile the applications of ML from the physical, chemical, microbiological, and temporal perspectives. From the physical perspective, ML is useful in managing drinking water production and demand and monitoring drinking water pipeline failures. From the chemical perspective, ML is promising in assessing and controlling .5 respectively; AMR, antimicrobial resistance; minHBa, minimum E-states for (strong) hydrogen bond acceptors; n X , number of halogen atoms; MDEC-24, molecular distance edge between all secondary and quaternary carbons; SssO, sum of atom-type E-state: -O-; meanI, mean intrinsic state values I;