Radiation oncology is a complex and highly technological area of medicine that relies heavily on digital data processing and computer software; New developments in offline and online MRI-guided RT are described in this paper, along with the exciting prospects they present for advancing research and clinical care, the challenges they present, and the necessity for interdisciplinary collaboration to address those challenges. One of the cornerstones of cancer care is radiation therapy (RT), which is still widely used today. Recently, a lot of work has gone into making MRI a regular part of clinical RT preparation and evaluation. MRI-guided RT is a hybrid technique that combines MRI’s capabilities with those of RT because to MRI’s enhanced contrast imaging of soft tissues, imaging of moving organs, and monitoring of physiological changes in tumors and other tissues. Clinical contexts can be evaluated quantitatively rather than qualitatively, allowing for the discovery of complicated patterns in medical data. Artificial Intelligence (AI) systems are ideally suited. As molecular imaging and personalized medicine have progressed it has interest in and importance placed on nuclear theranostic approaches, which can aid in providing individualized care for a wide range of illnesses by facilitating more accurate diagnosis, better prognosis forecasting, and more precise patient selection. In the case of prostate cancer (PC), imaging is utilized both to detect local recurrence for salvage therapy and to rule out metastases, which require systemic treatment. Hence this paper proposes AI based MRI-RT (AI-MRI-RT) to mitigate the challenges prescribed above. The purpose of this research is to investigate how prostate cancer radiation relates to diagnostic imaging and it provides a narrative and analytical report of the impact of cutting-edge approaches in the management of prostate cancer, particularly in pretreatment prognostication, radiation planning, and the incorporation of systemic therapy.