One of the areas where AI integration has revolutionized the oncology has been in personalized radiation therapy and in the precision oncology. Advancements of AI in medical imaging, predictive analytics and treatment planning support therapeutically tailored approaches driving efficacy while reducing adverse effects. There are machine learning algorithms using which tumor segmentation, dose optimization and adaptive radiotherapy are made precise to target the malignant tissues precisely without damaging the healthy structures. Also, while genomic analysis is powered by AI, it permits patient-dependent treatment suggestions that lead to higher response rates and lower go with trial and error coding within luxurious beginner most cancers control. Radiation dose prediction and automated contouring are then further refined by deep learning models and reinforcement learning techniques to reduce variability and increase reproducibility. Additionally, AI helps in the real-time monitoring of the treatment responses and, if essential within these treatment responses, incorporating modifications in the treatment protocols in real time. Its use however has its challenges including data privacy, interpretability of models and regulatory compliance. This paper goes on to explore how AI has helped to determine the role of AI in optimizing personalized radiation therapy as well as precision oncology by impacting clinical decision making and patient outcomes. These challenges can be addressed and AI driven innovations used to improve the survival rates and quality of life for cancer patients are possible realities in the future of oncology.