Abstract In this study, an innovative and intelligent computing regression algorithm, multivariate adaptive regression splines (MARS), was applied to simulate pesticide transport in soils. Using a divide-and-conquer method, the algorithm classifies the training data into several groups, in each of which a regression line or hyperplane is fitted. Compared to other intelligent computing technologies, MARS is fast, flexible, and capable of determining the important sequence of inputs to the output. This study evaluated MARS by applying it to simulate pesticide concentration levels at different soil depths and at various times. The model inputs included the number of days after pesticide application, accumulated rainfall, accumulated potential evapotranspiration, accumulated soil temperatures at depths of 100 mm in the morning as well as in the afternoon, and tillage practices. Several MARS models were developed to simulate the concentration levels of atrazine, deethylatrazine, and metolachlor at depths of 0–75 and 75–150 mm, respectively. The performance of MARS was compared to that of artificial neural networks (ANNs) using standard errors and correlation coefficients of linear regression. The results show the strong potential of MARS to be applied to agriculture as a regression technology.