Worldwide, type 2 diabetes mellitus (T2DM) prevalence is rising, and macrovascular complications remain its primary cause of death. Although glycemic control alleviates microvascular damage, risk factors for macrovascular complications remain unclear. Recent findings indicate that models built on real-world data outperform those based solely on clinical trials in forecasting T2DM outcomes. Using electronic medical records, we constructed XGBoost, decision trees, and random forest models to predict four macrovascular complications in T2DM. XGBoost achieved the highest accuracies—0.985 for hypertensive heart disease, 0.960 for ischemic heart disease, 0.956 for cerebrovascular disease, and 0.969 for coronary heart disease. These results can be incorporated into clinical workflows, streamlining information exchange for healthcare providers and patients.