Mechanical metamaterials (MMs), with their unique structural configurations rather than their chemical compositions, offer a wide range of unprecedented mechanical properties, from enhanced toughness and energy absorption to advanced vibration damping and soundproofing capabilities. Recent advancements in additive manufacturing have enabled the creation of these intricate geometries, leading to materials that excel in diverse applications, including safety gear, aerospace, noise-canceling technologies, and impact-resistant devices. Designing MMs today involves a complex, time-consuming iterative process where experts intuitively define designs, validate them with physics-based simulations, and refine them through trial and error. Recent advances in generative AI and machine learning offer the potential to disrupt this design cycle by automating and accelerating the process. This research proposes the development of a computational pipeline that integrates AI-driven optimization techniques with advanced simulations to streamline MM design. Using a graph-based representation of MM structures, the system effectively reduces the design space of general 3D MMs from hundreds of thousands of dimensions to a compact space with 10–100 dimensions. The system will incorporate advanced optimization techniques that learn efficient design solutions and enable the transfer of insights across different material properties, for example, from stiffness to shear strength or impact resistanc