Aim To evaluate the value of the monocyte-to-high density lipoprotein cholesterol ratio (MHR) combined with systemic inflammation response index (SIRI) in predicting the risk of heart failure (HF) readmission in elderly patients with type 2 diabetes mellitus (T2DM) and acute coronary syndrome (ACS), and to establish a risk prediction model. Methods In this retrospective cohort study, 775 elderly inpatients with T2DM and ACS hospitalized in the Affiliated Hospital of Inner Mongolia Medical University from January 2023 to October 2025 were enrolled. The primary endpoint was the first readmission for HF in our hospital from discharge to the data cutoff date. All patients were randomly divided into a training set (n＝542) and a test set (n＝233) at a ratio of 7∶3. LASSO regression followed by multivariate Logistic regression was used to identify independent predictors and construct the prediction model. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis, and a nomogram was established. The SHAP method was used to quantify the contribution of each predictor to the model. Results A total of nine independent predictors were identified:sex, history of primary stent implantation, history of secondary stent implantation, platelet count (PLT), low density lipoprotein cholesterol (LDLC), globulin (Glo), left ventricular ejection fraction (LVEF), MHR and SIRI. Multivariate Logistic regression showed that higher MHR (OR＝4.4,5%CI:2.972～6.704, P＜0.001) and higher SIRI (OR＝1.0,5%CI:1.015～1.823, P＝0.039) were independently associated with an increased risk of HF readmission, whereas higher LVEF (OR＝0.0,5%CI:0.233～0.439, P＜0.001) was independently associated with a reduced risk of HF readmission. In the test set, the Logistic regression model achieved an area under the curve (AUC) of 0.917, demonstrating good discrimination and calibration. SHAP analysis showed that MHR contributed most to the model prediction, followed by LVEF. ConclusionMHR combined with SIRI effectively predicts the risk of HF readmission in elderly patients with T2DM and ACS. The Logistic regression-based nomogram showed good predictive performance and potential clinical utility.