Cell and gene therapies (CGTs) offer curative potential for intractable diseases but face significant hurdles, including manufacturing costs exceeding $400,000 per dose, 40% clinical hold rates for IND applications, and lengthy vein-to-vein timelines. Artificial intelligence (AI) and machine learning (ML) are emerging as critical tools to optimize the CGT lifecycle. This review examines predictive modeling applications in target identification, vector engineering, manufacturing, and clinical outcome prediction. We discuss key methodologies such as deep learning, protein language models, and reinforcement learning. Case studies highlight the efficacy of these tools, notably Dyno Therapeutics’ AAV capsid engineering, which increased viability from <1% to 55%, and the AIDPATH digital twin initiative for decentralized manufacturing. Additionally, the review navigates the evolving regulatory landscape, including the FDA’s 2025 draft guidance on AI credibility. By integrating multi-omics data, quantum computing, and explainable AI (XAI), the next generation of CGTs aims for enhanced safety and scalability. This article provides a comprehensive roadmap for stakeholders to leverage AI in accelerating the development of advanced therapeutic medicinal products (ATMPs) while addressing essential ethical and data governance frameworks.