This review explores the significant impact of AI-driven technologies, particularly AlphaFold2, on drug discovery and development, with a focus on cancer research. AlphaFold2 and other AI algorithms have revolutionized protein structure prediction by enhancing the precision and speed of identifying drug targets and designing drug candidates. The review discusses the efficacy, limitations, and potential challenges of AlphaFold2, comparing it with other algorithms like ESMFold. It also examines the broader applications of these technologies, including the prediction of protein complex structures and the design of novel proteins using generative AI. The process of drug discovery is highlighted, emphasizing the inefficiencies and high costs associated with traditional methods. The review delves into the role of AI in understanding disease mechanisms, identifying therapeutic targets, and accelerating drug development. It also discusses the limitations and challenges, such as computational resources and regulatory issues, and the need for further advancements to improve the accuracy and applicability of AI tools in protein structure prediction. Overall, the review underscores the transformative potential of AI in advancing cancer drug discovery and development.This review explores the significant impact of AI-driven technologies, particularly AlphaFold2, on drug discovery and development, with a focus on cancer research. AlphaFold2 and other AI algorithms have revolutionized protein structure prediction by enhancing the precision and speed of identifying drug targets and designing drug candidates. The review discusses the efficacy, limitations, and potential challenges of AlphaFold2, comparing it with other algorithms like ESMFold. It also examines the broader applications of these technologies, including the prediction of protein complex structures and the design of novel proteins using generative AI. The process of drug discovery is highlighted, emphasizing the inefficiencies and high costs associated with traditional methods. The review delves into the role of AI in understanding disease mechanisms, identifying therapeutic targets, and accelerating drug development. It also discusses the limitations and challenges, such as computational resources and regulatory issues, and the need for further advancements to improve the accuracy and applicability of AI tools in protein structure prediction. Overall, the review underscores the transformative potential of AI in advancing cancer drug discovery and development.