Artificial intelligence (AI) is transforming oncology through advancements in diagnosis, treatment, and prognosis. Artificial intelligence (AI) is revolutionizing the field of oncology, offering advanced capabilities in diagnosis, treatment planning, and personalized medicine. The integration of AI in oncology has led to significant improvements in early detection, prognostic assessments, and optimized therapeutic strategies. Machine learning algorithms and deep learning networks, particularly in medical imaging and histopathology, are providing enhanced accuracy in tumor characterization and classification. Furthermore, AI is accelerating drug discovery by predicting drug-target interactions, optimizing clinical trial designs, and expediting the development of novel cancer therapies. Clinical decision support systems powered by AI are assisting oncologists in treatment selection by analyzing vast datasets from electronic health records, clinical trials, and real-world evidence. AI-based predictive analytics is also improving patient management, allowing for better treatment response predictions and personalized care strategies. Despite these advancements, several challenges hinder the full-scale integration of AI into oncology. Issues, such as data privacy, regulatory compliance, algorithm interoperability, and bias in AI models, must be addressed to ensure ethical and effective implementation. Additionally, the collaboration between AI developers, oncologists, and healthcare policymakers is crucial to overcoming these barriers and maximizing AI’s potential in cancer treatment. This review explores AI’s integration into oncology’s basic principles, public health, solid malignancies, hematological malignancies, and clinical applications. This study categorizes various research efforts and provides detailed tables summarizing datasets, methodologies, and findings. Key image datasets for cancer research are highlighted, supporting future innovations. Furthermore, this study includes clinical research works leveraging AI in oncology, categorized across different tracks.