Abstract: Synthetic biology combines engineering principles with biology, aiming to design and reprogram biological systems for applications in health, energy, and the environment. Recent advancements in artificial intelligence, particularly deep learning, are accelerating the development of medical synthetic biology through intelligent component design, metabolic pathway optimization, disease diagnosis, and advanced therapies. This paper first reviews the latest breakthroughs of artificial intelligence in protein engineering, genome editing, cell and microbial therapies, and vaccine design; secondly, it specifically presents representative cases such as AlphaFold-based CAR-T structure optimization and AI-guided adeno-associated virus (AAV) capsid evolution to improve gene therapy targeting, highlighting the transformative significance of the integration of artificial intelligence and synthetic biology; finally, it discusses key challenges such as data quality, model interpretability, biosafety, and ethics, and envisions future directions for AI-driven automated experimental platforms, multimodal modeling, and personalized medicine.