This study ventures into the intricate realm of aero engine mount systems, delving into the design and optimization of these crucial components. Our research utilizes mathematical modeling, computational algorithms, and a well-coordinated integration of Python scripting with Computer-Aided Design (CAD) tools to explore the design space of engine mounts, aiming to optimize their performance. Specifically, the study targets the optimization of certain design variables - L1, θ1, θ2, θ3, and R - that characterize the physical properties and performance of the engine mount system. The Python script computes the optimal values for these variables, which are then inputted into a CAD program, enabling the visualization and analysis of the optimized design. One of the fundamental objectives of this study was to minimize the forces experienced within the links of the engine mount system. The optimization procedure focused on the balance and distribution of forces across the links, ensuring that no single link was subjected to an undue portion of the load. The successful achievement of this objective not only improved the structural integrity of the engine mount system, but also underscored the potential of targeted optimization strategies in enhancing the performance of these critical components. By reducing the forces within the links, the study was able to contribute to the overarching goal of improving the overall distribution of loads in the separate links of the aero engine mount structure. The optimization objectives of this study also include minimizing the overall weight of the engine mount system, reducing backbone bending, and minimizing deflections through the reduction of the radial component. The results demonstrate the successful accomplishment of these objectives within the set boundaries, paving the way for enhancements in the structural rigidity and reliability of the engine mount system. Lastly, the study underscores the potential of leveraging computational optimization tools, such as the Python scripting and the L-BFGS-B algorithm. The outcomes of this study offer essential insights that could guide future design and optimization processes of engine mounts, laying a robust groundwork for further exploration in this field. Future work may include extending this methodology to larger engines with different behaviors and scales.  For those intrigued by the computational aspect of this investigation and keen to delve deeper into the intricacies of the code employed, Appendix A provides a comprehensive view. The Python script utilized in this study, integral to the optimization process, is included in its entirety in this section.