Recent advancements in artificial intelligence (AI) technologies have revolutionized the era of cancer drug development and immunotherapy by enhancing precision and speed. The use of AI in the drug development process leads to the growth of potential drug candidates, thus increasing the effectiveness of the preclinical and clinical testing. AI-driven home quest in cancer target identification is essential; the role of AI is multifaceted. The AI in immunotherapy is used to analyze patients' responses to checkpoint inhibitors and to identify predictive biomarkers that aid in personalized medicine. This chapter highlights how recent advancements in AI are transforming cancer drug development and immunotherapy by enabling faster target identification, cost-effective drug discovery, and personalized treatment strategies. It emphasizes the role of AI-driven approaches, such as ML and DL, in multi-omics integration, biomarker prediction, CAR–T optimization, and virtual compound design, while also addressing challenges like data availability and collaboration.