NN-based and regression-based ML models are the top two frequently implemented in safe drinking water supply. NN-based models have significant applications in managing the production and demand of drinking water and accessing and controlling DBPs. The prominent role of NNbased models in these two fields is not coincidental but rather from the synergy between the inherent characteristics of these fields and the strengths of NN-based models. Water management and DBP assessment often involve multifaceted, nonlinear, and high-dimensional data that demand robust modeling . Given their capability to model complex non-linear relationships and handle various intricate data, NN-based models are an optimal solution in these contexts. For instance, the unpredictability and variability in water demand patterns or the multifarious factors influencing DBP formation both require a model that can discern patterns from large, intricate datasets . Furthermore, the flexibility of NN-based models in accommodating changing inputs makes them promising in assessing the dynamic nature of drinking water quality. The wide applications of NN-based models in safe drinking water supply are due to this harmonious fit between the challenges posed by these fields and the advantages of these models.