Despite the fact that immunologic rejection offers a considerable danger to long-term graft survival, organ transplantation continues to be a treatment that may save the lives of patients who are going through the latter stages of organ failure.  Traditional techniques of immunologic risk assessment, such as matching with human leukocyte antigen (HLA) and testing with panel-reactive antibody (PRA), have limitations when it comes to predicting transplant rejection.  A revolution in healthcare has occurred as a result of developments in artificial intelligence (AI) and machine learning (ML), which have made it possible to do predictive analytics, tailor medical treatment, and enhance risk stratification. Artificial intelligence-driven models include data from several omics, deep learning algorithms, and predictive modelling in order to give physicians with accurate and timely insights that can be used for decision-making.  Artificial intelligence-driven systems have the potential to revolutionise transplant medicine by refining immunosuppressive medication and increasing patient outcomes. This is despite the fact that there are hurdles such as data bias, interpretability, and regulatory issues.