This paper presents a hybrid optimization framework that integrates Multi-Physics Topology Optimization (MPTO) with a Neural Network–surrogated Design of Experiments (NN-DOE) to enable lightweight structural design while satisfying crashworthiness, durability, and noise, vibration, and harshness (NVH) requirements under practical casting and packaging constraints. In the proposed MPTO formulation, crash and durability performances are incorporated through equivalent static compliance measures, while NVH performance is assessed using a frequency-domain dynamic stiffness metric, allowing consistent evaluation of trade-offs among competing design requirements. The framework is first demonstrated using a mass-produced passenger-car lower control arm (LCA) as a benchmark component. In this application, MPTO achieves weight reduction under multi-physics objectives by removing non-load-bearing material. Results show that single-discipline optimization produces unbalanced topologies, while balanced crash–durability–NVH consideration yields robust load paths. The study further demonstrates that crash and durability are dominated by static compliance–based response, whereas NVH performance is governed by frequency-dependent dynamic response over the relevant frequency range. The framework is then applied to a front engine mounting bracket of a newly developed heavy-duty truck. In this second application, a two-step strategy is employed in which MPTO first establishes the global load-carrying topology under manufacturing and packaging constraints, followed by NN-DOE–based local refinement to achieve stress attenuation at non-designable regions through global structural stiffness rebalancing, rather than direct geometric modification. Final verification confirms a steel-to-aluminum material transition achieving approximately 45% weight reduction and a substantial improvement in durability fatigue life, while maintaining required crash performance.