Osteochondral tissue engineering (OCTE) faces significant challenges due to the complex gradient architecture of the osteochondral unit and the limitations of traditionally designed scaffolds. Existing approaches, including hydrogels, composites, and 3D bioprinting, have advanced the field, yet problems such as insufficient vascularization, poor mechanical stability, and unsuccessful clinical translation remain unresolved. In parallel, artificial intelligence (AI) has experienced rapid growth in biomaterials research, but its application to OCTE scaffolds is virtually absent—only a single study has been identified that explicitly bridges these two domains. This chapter provides the first systematic review of this research gap, combining qualitative and quantitative literature analyses (2010–2025) and highlighting key growth patterns and deficiencies at the intersection of AI and OCTE. Based on these findings, this chapter proposes a methodological framework built on generative models for scaffold architecture design and hybrid in silico–in vitro feedback loops. The perspective offered underscores the need for dataset standardization, the creation of benchmark tasks, and robust interdisciplinary collaboration between AI researchers, biologists, and biomaterials engineers. In conclusion, integration of AI into OCTE could become a driver of the next generation of regenerative medicine, with immense potential for translational and clinical benefits.