Radiotherapy (RT) planning uses a 3D image of the region to be treated (i.e. CT scan), and a complex computational process for defining the RT beams’ charachteristics (i.e. energy, shape) required to ensure the cancer gets the prescribed radiation dose while surrounding normal tissues get as little as possible. Generating high-quality RT plans demands specialized expertise and significant time investment (i.e. hours to days) for each individual plan. However, variability of these factors results in discrepancies within/between Institutions, exposing patients to sub-optimal plans that could jeopardize their treatment’s results (i.e. decreased cure, increased side-effects). We have developed a unique method that capitalizes hundreds of peer-reviewed high-quality plans, and progressively learns without being explicitly programmed (i.e. machine-learning). The method collapses RT planning to only 15-25minutes without necessitating user input, rendering RT plans of consistent and reproducible quality. In this proposal, we will generate prostate cancer (PCa) specific libraries and train specific planning algorithms. Subsequently, we will use the automated planning framework for upcoming patients and compare its performance to the conventional user-based method. We aim to unveil a new RT planning method for PCathat makes the most out of reference centers’ expertise, reduces variability and errors, and enables highly individualized and cost-effective PCacare.