INTRODUCTION: Patients with type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD) face substantially elevated coronary heart disease (CHD) risk, yet no machine learning diagnostic models exist specifically for this population. This study aimed to develop and validate an interpretable machine learning model for identifying CHD in T2DM-MASLD patients. METHODS: Using data from 1,269 patients (development cohort) and 1,058 patients (external validation cohort) from two Chinese hospitals, we compared seven machine learning algorithms. Angiographically confirmed CHD served as the diagnostic endpoint. Nine features were selected by univariate analysis, LASSO regression, and the Boruta algorithm. The best-performing model was selected based on comprehensive evaluation of discrimination, calibration, and clinical utility. Model interpretability was assessed using SHapley Additive exPlanations (SHAP), and external validation was performed in an independent cohort. RESULTS: Feature selection identified nine predictors: total cholesterol (TC), chest distress, apolipoprotein B (ApoB), male sex, triglycerides (TG), age, chest pain, red cell distribution width (RDW), and cardiac troponin (cTn). The XGBoost model achieved the best performance, with an AUC of 0.896 (95% CI, 0.862-0.930) in internal validation and 0.865 (95% CI, 0.837-0.893) in external validation, with excellent calibration (Brier score: 0.112). To facilitate clinical application, a freely accessible web-based calculator was developed for real-time individualized CHD risk prediction. DISCUSSION: This is the first interpretable machine learning model externally validated for CHD diagnosis in T2DM-MASLD patients, demonstrating robust performance using nine routinely available clinical parameters. The model's interpretability through SHAP analysis enhances clinical trust and supports individualized risk communication between physicians and patients to guide decisions regarding coronary angiography.