The increasing use of ML in safeguarding drinking water quality has led to the development of innovative approaches to detect drinking water quality from the temporal perspective (Table ) . The term 'temporal' refers to the time-related applications of ML to track, predict, and mitigate contamination events in drinking water as they unfold over time. By examining the temporal patterns and trends of contamination events, we can bolster the predictive power and responsiveness of ML to ensure effective measures against accidental contamination in drinking water. This section explores the advancements, versatility, and potential of ML in