Kidney transplantation offers life-extending treatment for patients with end-stage renal disease, yet long-term risks of graft loss and death persist. Traditional prediction models using only baseline data often fail to capture patients' evolving health status post-transplant. In this study, we propose a two-stage machine learning (ML) framework for dynamic, next-year risk prediction of graft loss and death, updated annually with newly available clinical and laboratory data. Using a multi-center cohort from the Swiss Transplant Cohort Study (STCS), we trained and evaluated five ML models across 13 years of follow-up, demonstrating that incorporating longitudinal data significantly improved predictive performance compared to baseline-only models. LightGBM achieved the strongest performance, with AUROC values up to 0.896 for graft loss and 0.797 for death. Our findings suggest that dynamic, interpretable ML models can enhance personalized risk stratification, offering a practical and scalable tool for guiding follow-up strategies and early interventions in kidney transplant recipients.