The crashworthiness optimization of closed-cell aluminum foam-filled sandwiched crash boxes is a critical aspect of vehicle of occupant safety, aimed at enhancing the energy absorption capability of these structures during collisions. The crash box is designed to absorb kinetic energy during frontal impacts and keep vehicle deceleration within acceptable limits. Therefore, careful consideration should be given to crash box design to reduce the occurrence of the vehicle suffering significant damage in a frontal accident. Aluminum Foam sandwich structure is a lightweight energy absorber and enhanced crashworthiness capability. This optimization process involves using advanced computational techniques such as Finite Element Analysis (FEA) simulation, specifically through software like Abaqus, combined with Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for further analysis. In the optimization of crashworthiness for a closed-cell aluminum foam-filled sandwiched crash box, the process begins by modeling the foam geometry using Digimat (MSC), by considering factors such as cell size, porosity, and density. The model is then exported to Abaqus for further analysis. By Utilizing Minitab (Taguchi), a Design of Experiment (DOE) is conducted with 27 runs involving three factors to assess responses. Variance analysis (ANOVA) determines the significance between factors and response, focusing on energy absorption. After analysis the maximum energy absorption of 255J is identified from 27 runs, achieved with a combination of cell size, porosity, and density of (10, 15, and 2.6). To optimize energy absorption and determine optimal parameters, results from Abaqus are input into Artificial Neural Network (ANN) model. The ANN generates a fitting function with a high R-value (0.989) and minimum error (1.34). The fitness function is then exported to a Genetic Algorithm (GA) optimization tool, refining it to achieve an optimized energy absorption of 256.69J. The optimal parameters identified through this process are cell size 10, porosity 0.162, and density 2.6. This integrated approach demonstrates how advanced computational tools and methodologies can be employed to enhance the crashworthiness of structural components effectively. Key words: ENERGY ABSORPTION, CELL-SIZE, POROSITY, DEFORMATION GA OPTIMIZATION, ANN