This review explores the application of machine learning (ML) in cancer research, focusing on technologies such as convolutional neural networks, decision trees, and generative adversarial networks. The authors examine how ML can improve cancer diagnosis, treatment planning, and patient survival prediction. They also discuss the potential of ML in medicine, emphasizing the need for data anonymization. The review highlights recent advancements in oncology, including the use of radiomics for image analysis, treatment planning, and drug synthesis. It discusses the role of ML in improving diagnostic accuracy, particularly in fast-progressing cancers, and the challenges of processing large medical data volumes. The review also addresses ethical and legal issues in ML applications in medicine. Key findings include the effectiveness of ML in predicting patient outcomes, improving diagnostic accuracy, and enabling personalized treatment strategies. The authors emphasize the importance of developing clear criteria and evaluation standards for ML use in cancer diagnosis. The review covers various ML techniques, including supervised learning, and their applications in different cancer types. It also discusses the integration of ML with other technologies, such as CRISPR, for gene editing in oncology. The review concludes that ML has significant potential to advance cancer research and treatment, but further research is needed to address ethical and practical challenges.This review explores the application of machine learning (ML) in cancer research, focusing on technologies such as convolutional neural networks, decision trees, and generative adversarial networks. The authors examine how ML can improve cancer diagnosis, treatment planning, and patient survival prediction. They also discuss the potential of ML in medicine, emphasizing the need for data anonymization. The review highlights recent advancements in oncology, including the use of radiomics for image analysis, treatment planning, and drug synthesis. It discusses the role of ML in improving diagnostic accuracy, particularly in fast-progressing cancers, and the challenges of processing large medical data volumes. The review also addresses ethical and legal issues in ML applications in medicine. Key findings include the effectiveness of ML in predicting patient outcomes, improving diagnostic accuracy, and enabling personalized treatment strategies. The authors emphasize the importance of developing clear criteria and evaluation standards for ML use in cancer diagnosis. The review covers various ML techniques, including supervised learning, and their applications in different cancer types. It also discusses the integration of ML with other technologies, such as CRISPR, for gene editing in oncology. The review concludes that ML has significant potential to advance cancer research and treatment, but further research is needed to address ethical and practical challenges.