Within healthcare, predictive frameworks serve an essential role in determining disease intensity through lifespan evaluation. Nonetheless, current systems lack precision in estimating results following renal transplantation. Considering the critical importance of this operation, a dependable and highly accurate framework is vital for anticipating recipient survival after the procedure. To bridge this shortcoming, a superior prediction mechanism was constructed to assess survival following kidney grafting. Drawing from the United Network for Organ Sharing records and applying ten-fold validation, a notable enhancement in prediction precision was realized. By employing a DeepLearning4j Multilayer Perceptron algorithm, our structure attained a remarkable 91.87% accuracy in forecasting three-month post-transplant survival. Additionally, we extended our analysis to project survival outcomes over a span of 35 years, maintaining commendable performance through the same technique. To confirm the model’s robustness, we benchmarked it against traditional frameworks and executed in-depth simulations to evaluate each individual's long-range survival potential. Moreover, we aligned model outcomes with authentic survival data, affirming that the DeepLearning4jMLP algorithm is highly appropriate for extensive post-transplant survival evaluation.