In water and wastewater engineering systems, hydrolysis is one of the most important means in abiotic degradation of disinfection byproducts (DBPs). Enhanced knowledge of hydrolysis of DBPs can help determine the likelihood of DBP occurrence in water and their risks. In order to better understand the roles that functional groups play in the fate and occurrence of DBPs in the environment, this study developed several quantitative structure-activity relationship (QSAR) models to estimate the hydrolysis rate constants for a pool of 40 halogenated, small molecular weight DBPs originating from seven chemical classes. The models are based on the descriptors calculated and filtered by stepwise discriminant analysis with multiple-linear regression (MLR) and artificial neural network (ANN) algorithms, and overall they exhibited better performance than models using conventional descriptors only. The relative importance of selected descriptors are ranked as average mole weight > number of fluorine > polar effect of functional group > mole weight > number of chlorine > dipole moment > the ratio of oxygen to carbon atoms. The squared regression coefficients between predicted and experimental values were 0.939 and 0.976 for the final MLR and ANN models, respectively. While the ANN model demonstrated better performance than the MLR model for all 40 DBPs, the MLR model shows more accurate predictions for haloethanes, haloacetic acids, and haloacetamides. Unlike existing QSAR models, which treat each type of DBP separately, the models developed in this study are unique in considering multiple types of DBPs together; therefore, these models may help researchers to better understand the effects of not only halogens but also functional groups on DBP stability.