<p>Moisture damage in asphalt mixtures leads to significant challenges in infrastructure durability, and accurate modeling is essential for effective strategies due to the complex nature of moisture susceptibility. Current tests, such as those utilizing general indicators like the indirect tensile strength ratio, examine moisture susceptibility in asphalt mixtures. However, these tests have significant costs and require considerable time. Therefore, the development of predictive models can yield substantial benefits. Accordingly, this study aims to develop moisture susceptibility prediction models using Gene Expression Programming (GEP) and Multigene Genetic Programming (MGGP). The dataset is utilized to predict two indicators of moisture susceptibility performance: Inflection Stripping Point (ISP) and Stripping Slope (SS). The ISP predictions yield R² values of 0.981 and 0.956 for MGGP and GEP models, respectively, while SS predictions result in R² values of 0.974 and 0.928 for MGGP and GEP models, respectively. The models utilized in this research can provide mathematical formulas that include the input parameters influencing ISP and SS. A parametric study was conducted on the best model, MGGP, wherein the behavior of its variables and their impact on ISP and SS was investigated.</p>