The integration of Artificial Intelligence (AI)-enabled multimodal Computed Tomography (CT)/Magnetic Resonance Imaging (MRI) medical imaging and 3D printing technology stands as a pivotal direction in medical engineering for advancing precision diagnosis and therapy. Multimodal data fusion represents the primary strategy to enhance the accuracy of 3D-printed models; however, cross-modal data fusion is plagued by inherent technical challenges, including failure in feature alignment and discrepancies in physical properties of imaging datasets. In recent years, the advancement and seamless integration of AI technology have emerged as the core link bridging the entire workflow from multimodal CT/MRI imaging acquisition to 3D printing, offering novel paradigms for the development of high-precision 3D printing technology in clinical settings. This review systematically elaborates on AI’s core technical underpinnings for multimodal imaging and 3D printing: AI effectively mitigates integration and adaptation hurdles arising from intrinsic discrepancies in data source characteristics through three key pathways—artifact reduction and optimization of raw imaging data, precise cross-modal registration, and fine-grained segmentation of anatomical structures. Furthermore, AI-driven optimization of 3D rendering effects, combined with four-view projection, significantly enhances the fidelity of anatomical detail reproduction, thereby minimizing the matching error between 3D-printed models and in vivo physical entities. Subsequently, the review details the clinical application value of multimodal 3D printing technology across key medical specialties, including orthopedics, oncological surgery, dentistry, and vascular surgery, while concomitantly highlighting prevailing challenges in technical translation and clinical adoption. Finally, it prospects future development directions from three critical dimensions: technological synergy (between AI, imaging, and 3D printing), material advancement (targeting durability and functional adaptability), and application expansion (to under-served clinical scenarios such as rehabilitation). This work aims to provide a comprehensive reference for accelerating the clinical translation of this interdisciplinary technology.