The development of radiation therapy has made cancer treatment a lot better, but there are still problems to solve in terms of making treatment more precise, effective, and safe. The development of artificial intelligence (AI) has opened the door to new ways of designing and using smart radiation treatment systems. By use of AI-driven optimization techniques, these systems enable more patient-specific treatment planning and administration, therefore reducing side effects and improving therapeutic efficacy. By allowing treatments to be more customized and adaptable, researchers believe that integrating artificial intelligence into radiation therapy systems might alter how physicians treat cancer. In radiation treatment, artificial intelligence-powered optimization begins with precise and efficient planning of treatment dosages and beam configurations. Deep learning and reinforcement learning, among other machine learning techniques, examine patient data including tumour traits and healthy cells around them in order to create tailored treatment regimens. These initiatives use historical treatment data and patient performance to determine which treatment plan would be most suited for every individual. AI may reduce human error by assuming control of the planning process and hasten the procedure, therefore enabling more precise and timely treatment. Not only can artificial intelligence schedule treatments, but it can also enhance real-time delivery of radiation. Using artificial intelligence, adaptive radiation therapy (ART) devices modify the radiation dosage and beam angle throughout treatment to consider changes in the patient’s body as well as the size, shape, and location of the tumour. AI algorithms can continuously monitor and estimate how the tumour will migrate using data from imaging devices as MRIs, CT scans, or real-time motion monitoring. This makes sure that the radiation is given with the utmost accuracy during the whole treatment session. This flexible, patient-centered method helps protect healthy cells around the wound, which raises the total healing score. Adding AI to smart radiation therapy tools could also help predict how patients will do and improve their care after treatment. Machine learning models can look at clinical data, like a patient’s treatment history and genetic information, to guess how the treatment will work and spot any possible side effects early on. With these new insights, doctors can make smart choices about how to change the amount, provide follow-up care, and handle patients. Also, AI can make it easier to find new signs and predictors of medical success, which will help the field of personalised medicine even more.