People with kidney failure face high risks of cardiovascular problems and death after non-cardiac surgery, but current tools to predict these risks do not work well. This study developed new machine-learning-based risk prediction models to help identify which patients are at higher risk after having non-cardiac surgery. The models used a small number of easily available variables such as type of surgery, surgery setting, and lab results. We tested the models by using data from two Canadian provinces and found them to be accurate and reliable. These models may allow clinicians to inform patients of their individualized risk, which may support shared perioperative decision-making. More research is needed to see how they perform in other geographic locations and healthcare systems.