Concerns regarding disinfection byproducts (DBPs) in drinking water persist, with measurements in water treatment plants (WTPs) being relatively easier than those in water distribution systems (WDSs) due to accessibility challenges, especially during adverse weather conditions. Machine learning (ML) models offer improved predictions of DBPs in WDSs. This study developed multiple ML models to predict Trihalomethanes (THMs), Haloacetic Acids (HAAs), Dichloroacetonitrile (DCAN), and N-nitrosodimethylamine (NDMA) in WDSs using data collected over 13 years (2008-2020) from 113 water supply systems (WSS) in Ontario. Data were collected tri-monthly (four times/year) following Ontario's regulatory requirements. Four common ML models-linear regressor (LR), random forest regressor (RFR), support vector regressor (SVR), and artificial neural networks with multiple folds cross-validation (ANN-MV) and single fold validation (ANN-SV)-were trained and tested using different datasets. R