Drug development must comply with relevant regulatory requirements and be guided by the relevant guidances for industry developed by regulatory agencies, with reference to relevant use cases developed by regulatory agencies. The approval of new drugs must be based on evidence generated from clinical trials. With the continuous development of the biopharmaceutical industry, the complexity of clinical trials has increased. Merely increasing investment is insufficient to improve clinical trial efficiency, and addressing issues of low pipeline productivity and soaring costs. This paper explores the application and regulation challenges of artificial intelligence/machine learning (AI/ML) in clinical trials by analyzing the viewpoints of FDA discussion paper and EMA reflection paper and stakeholders’ comments.