Before drugs can be authorized for sale, their effectiveness and safety must be documented through rigorous testing, as regulated by national and international agencies. This chapter discusses how AI is transforming drug evaluation by integrating virtual models with traditional methods. The regulatory framework ensures that drugs provide therapeutic benefits and document side effects. It covers drug development, clinical trials, and post-marketing surveillance. Regulations may adapt to encourage innovation, especially for rare diseases or new technologies. AI’s role in health requires evolving regulations and new tools to foster innovation while ensuring patient safety, with emerging flexible approaches such as regulatory sandboxes. AI is transforming clinical studies by optimizing design, implementation, and analysis, thereby increasing the success rate of drug candidates. AI aids in patient selection, data interpretation, and virtual modeling, thereby reducing costs and enhancing efficiency. Virtual patients and digital twins are being used for the predictive simulations, complementing the traditional trials. Regulatory acceptance of AI-driven models is growing, promoting a mixed reality approach to drug evaluation. AI also enhances drug safety by analyzing toxicological data and predicting side effects while further enhancing pharmacovigilance, thereby ultimately refining the benefit-risk ratio.