This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting, runner system optimization, and structural analysis to significantly enhance the performance of injection-molded parts. At its core, the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles, leading to improvements in both mechanical strength and material efficiency. The design optimization is validated through a series of rigorous experimental tests, including three-point bending and torsion tests performed on key-socket frames, ensuring that the optimized designs meet practical performance requirements. A critical innovation of the framework is the development of the Adjacent Element Temperature-Driven Prestress Algorithm (AETDPA), which refines the prediction of mechanical failure and strength fitting. This algorithm has been shown to deliver mesh-independent accuracy, thereby enhancing the reliability of simulation results across various design iterations. The framework’s adaptability is further demonstrated by its ability to adjust optimization methods based on the unique geometry of each part, thus accelerating the overall design process while ensuring structural integrity. In addition to its immediate applications in injection molding, the study explores the potential extension of this framework to metal additive manufacturing, opening new avenues for its use in advanced manufacturing technologies. Numerical simulations, including finite element analysis, support the experimental findings and confirm that the optimized designs provide a balanced combination of strength, durability, and efficiency. Furthermore, the integration challenges with existing injection molding practices are addressed, underscoring the framework’s scalability and industrial relevance. Overall, this hybrid topology optimization framework offers a computationally efficient and robust solution for advanced manufacturing applications, promising significant improvements in design efficiency, cost-effectiveness, and product performance. Future work will focus on further enhancing algorithm robustness and exploring additional applications across diverse manufacturing processes.