The well-trained MLR model has a promising prediction accuracy with an R 2 value of 0.91 and an RMSE value of 8.93 mol/mol. Further chemical insights pinpoint that functional groups, such as hydroxyl, chlorine, and carboxyl groups, significantly affect chloroform formation. ML can also forecast the formation of HAAs from the interaction between organic precursors and free chlorine . The training dataset comprises 283 organic compounds and 732 chemical descriptors as the inputs with HAA yield as the output. These organic compounds are converted into 2D and 3D chemical descriptors with their simplified molecular input line entry system (SMILE) strings used for ML compatibility. Three ML models (RF, SVR, and MLP) are selected because they can handle nonlinear problems, activity cliffs, and high dimensions in addition to MLR as a benchmark. RF is the top performer with the lowest RMSE values of 1.05 and 1.19 for dichloroacetic acid (DCAA) and trichloroacetic acid (TCAA), respectively. The crucial predictors of TCAA formation are the number of aromatic bonds, hydrophilicity, and electrotopological descriptors related to electrostatic interactions and the atomic distribution of electronegativity.