A study developed three ML models, including ANN, SVM, and gene expression programming (GEP), to forecast THM formation in chlorinated river water on the basis of a 63-point dataset . Specifically, pH, water temperature, contact time (t), bromide (RBF-ANN) can predict the formation of typical DBPs such as HAAs , THMs , and HKs in drinking water. For instance, a study extracted 64 representative data points from the literature to predict HAA formation using pH, water temperature, DOC, UV254, , , , and as the inputs (Figure ) . RBF-ANN outperforms the linear and log-linear models by 21% and 47 % in accuracy, respectively. Therefore, RBF-ANN is promising in assessing DBP formation and optimizing disinfection. A follow-up study used 64 data points to train an RBF-ANN to predict THM formation . RBF-ANN achieves accuracies between 92% and 98% and regression coefficients between 0.76 and 0.93, outperforming the linear and log-linear models and demonstrating its superiority to uncover complex non-linear patterns in THM formation. Even when trained with fewer water quality variables, a fusion of grey relation analysis with RBF-ANN could provide superior prediction results. Furthermore, an RBF-ANN trained with 63 data points of tap water predicts the formation of HK . Both RBF-ANN and back propagation (BP) ANN outstrip the linear and log-linear models with the RBF-ANN displaying higher accuracies in both internal and external validations. Another study applied a decision tree boost (DTB) model to predict the concentrations of THM4 and HAAs . The study correlated water quality parameters with mixed chlorine/chloramine species. The work then selected seven variables such as NH2Cl, NHCl2, organic chloramines, pH, total dissolved nitrogen (TDN), nitrite, total organic carbon (TOC), and NH4 + as the independent variables to predict THM4 and HAAs. The DTB model demonstrates higher prediction accuracy with R 2 values of 0.56 for THM4 and 0.65 for HAAs, while the inclusion of organic chloramines improves the prediction. Additionally, a study implemented multiple ML models to predict emerging DBPs in small water distribution systems across Canada by analyzing the data from eleven such networks