Objective To investigate risk factors for postoperative sepsis‑induced myocardial dysfunction (SIMD) in patients with sepsis and to construct a nomogram model predicting SIMD occurrence in postoperative sepsis patients. Methods Retrospective analysis was conducted on 128 patients who were diagnosed with sepsis and underwent surgical drainage before transfer to Department of Critical Care Medicine, the People's Hospital of Xinjiang Uygur Autonomous Region from January to December 2021. Their demographics and laboratory data were collected. Based on whether SIMD occurred during treatment, the patients were divided into two groups: a myocardial injury group (n=47) and a control group (n=81). Multivariate logistic regression analysis was conducted to identify independent risk factors for postoperative SIMD in sepsis patients, based on which a predictive nomogram model was constructed. The clinical applicability of the nomogram model was analyzed and internally validated. Results The myocardial injury group showed increases in age, platelet (PLT), aspartate aminotransferase (AST), lactate (Lac), B‑type natriuretic peptide (BNP), cardiac troponin T (cTnT), creatine kinase‑MB (CK‑MB), Sepsis‑related Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHEⅡ) score and mortality rate compared with the control group (all P<0.05), as well as extended surgical intervention time compared with the control group (P<0.05). Univariate logistic regression analysis was conducted, using statistically significant variables as independent variables, and the occurrence of myocardial injury as the dependent variable, and BNP, CK‑MB, SOFA score, and delayed surgical intervention time were identified as independent risk factors for SIMD (all P<0.05). Using these four predictors, a nomogram prediction model was established. When the prediction threshold was 0.02‒0.83, the net benefit rate of the prediction model was higher than that of both full intervention and no intervention, suggesting good clinical applicability of the nomogram. The area under the curve (AUC) values before and after internal validation were 0.895 [95% confidence interval (CI) 0.838, 0.952] and 0.891 (95%CI 0.826, 0.941), with sensitivities of 0.852 and 0.812, and specificities of 0.830 and 0.803, indicating good discriminative ability of the model. Conclusions The individualized risk prediction model for SIMD based on independent risk factors for postoperative SIMD in sepsis patients has clinical applicability. It effectively predicts the risk of postoperative SIMD in sepsis patients, facilitates early identification of high‑risk individuals, and may improve outcomes.