. The models use parameters such as water temperature, total chlorine residual, DOC, turbidity, pH, conductivity, and UV254 to predict the concentrations of THMs, HAAs, dichloroacetonitrile (DCAN), chloropicrin (CPK), and trichloropropanone (TCP). Among the evaluated models, support vector regression (SVR) and Gaussian process regression (GPR) show superior performance with SVR exhibiting the highest prediction accuracy (R 2 = 0.94) and stability for DCAN and TCP, while GPR is optimal for predicting CPK (R 2 = 0.92).