The pesticides and active pharmaceutical compounds in water can potentially causedamage, including theincreased cancer risk; liver, and kidney. A quantitative structure–retention relationship (QSRR) was developed using the partial least square (PLS), Kernel PLS (KPLS), and Levenberg-Marquardt artificial neural network (L-M ANN) approach for chemometrics study. The data contained retention time (RT) of the 87 pesticides and active pharmaceutical compounds in wastewater and river waters. Genetic algorithm was employed as a factor selection procedure for PLS and KPLS modeling methods. The results showed that, the GA-PLS descriptors are selected for L-M ANN. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN.