This review examines the latest advancements in using machine learning (ML) methods, including convolutional neural networks, decision trees, and generative adversarial networks, to address various challenges in cancer diagnosis and treatment. The authors discuss the potential of ML in medical image analysis, treatment planning, patient survival prognosis, and drug synthesis. They highlight the importance of addressing ethical and legal issues, such as data anonymization, in the application of ML technologies in medicine. The review also covers specific applications of ML in radiomics, nuclear medicine imaging, and personalized medicine, emphasizing the role of ML in improving diagnostic accuracy, predicting treatment outcomes, and enhancing patient care. Despite the growing interest in AI and ML in medical practice, the authors note that unresolved ethical and legal challenges remain, which need to be addressed for the full realization of these technologies in healthcare.This review examines the latest advancements in using machine learning (ML) methods, including convolutional neural networks, decision trees, and generative adversarial networks, to address various challenges in cancer diagnosis and treatment. The authors discuss the potential of ML in medical image analysis, treatment planning, patient survival prognosis, and drug synthesis. They highlight the importance of addressing ethical and legal issues, such as data anonymization, in the application of ML technologies in medicine. The review also covers specific applications of ML in radiomics, nuclear medicine imaging, and personalized medicine, emphasizing the role of ML in improving diagnostic accuracy, predicting treatment outcomes, and enhancing patient care. Despite the growing interest in AI and ML in medical practice, the authors note that unresolved ethical and legal challenges remain, which need to be addressed for the full realization of these technologies in healthcare.