<p>Direct field measurements of infiltration parameters are challenging, time-consuming, costly, and spatially restricted. As an alternative, traditional empirical models are often employed for the sake of simplicity; however, they lack both accuracy and adaptability. This study proposes a hybrid approach leveraging various Machine Learning (ML) techniques, including Deep Learning (DL) models, and Genetic Algorithms (GA) optimization to predict infiltration rate f(t) and cumulative infiltration F(t). Four models were developed: linear regression, non-linear regression, a standalone Convolutional Neural Network (CNN), and a GA-optimized CNN. Experimental data (time, f(t), F(t), moisture, and soil types) were split into 70% training and 30% testing sets. Model performance was validated using a combination of statistical metrics and graphical tools, including scatter plots and Taylor diagrams. Results show that using GA optimization significantly reduced errors across all models, with time identified as the dominant factor influencing infiltration, while soil moisture and texture showed negligible impact in the Madjez Ressoul watershed. The optimized CNN consistently achieved the highest accuracy for f(t) and F(t) estimation. Overall, the integration of AI -driven modeling, particularly GA-enhanced DL architectures, represents a significant advancement in hydrological prediction, and offering a scalable and precise framework for managing water resources in Mediterranean environments.</p>