In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specifc gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. Te efect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. Te results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). Te ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. Te SA revealed diferent importance by the GEP and MEP models, however, its confrmed that the maximum dry density and optimum moisture content signifcantly afect the Ps-ES. Te variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efciently lessens the laborious and timeconsuming testing.