This study explores a methodology for the topological optimization of car modules by integrating TRIZ (Theory of Inventive Problem Solving) and machine learning techniques. Initially, TRIZ principles guide the qualitative optimization phase, establishing proper design directions aimed at weight reduction and durability enhancement. Following this, machine learning tools, including ARRK’s proprietary algorithms, are applied for precise parametric optimization, ensuring alignment with performance criteria. The findings demonstrate the efficacy of this integrated approach, significantly improving car module design by refining geometrical proportions and achieving dual objectives: weight reduction and enhanced strength. While the study highlights the potential of combining TRIZ and machine learning, it acknowledges limitations due to the use of freely available 3D models and the proprietary nature of certain algorithms. Nonetheless, this research provides a comprehensive framework for automotive engineers and designers, setting a new benchmark for incorporating qualitative insights into the quantitative optimization of complex systems.