This comprehensive overview delves into the intricate intersection of multi-material additive manufacturing (MMAM) and machine learning (ML), exploring their synergistic applications and addressing inherent challenges. The article unfolds the potential of ML in optimizing MMAM processes, particularly in designing metamaterials with unique properties. Focusing on structural engineering, it delineates ML’s impact on computational cost reduction, design resolution enhancement, and predictive performance improvement. The integration of ML with MMAM is exemplified through various studies, showcasing ML algorithms’ effectiveness in predicting stress–strain curves, material strength, and optimization of mechanical metamaterials. The article navigates through the challenges of metal–metal, metal–ceramic, metal–polymer, and polymer MMAM, elucidating the complexities of material compatibility, bonding, and dimensional accuracy. ML emerges as a crucial ally in overcoming these challenges, offering solutions in predicting material interactions, optimizing parameters, and ensuring robust bonds between dissimilar materials. Emphasizing the evolving landscape of 3D printing, the article explores the revolutionary strides in metal–ceramic and metal–polymer MMAM, underlining the role of ML in optimizing the design process. It concludes by underscoring the transformative potential of ML in advancing MMAM, fostering innovation, and overcoming limitations in multi-material extrusion-based rapid prototyping.