Objective Patients with acute coronary syndrome （ACS） combined with chronic kidney disease （CKD） are at high risk of major adverse cardiovascular events （MACE）， and early prediction is crucial for improving prognosis. Non-invasive detection methods， due to their simplicity and safety， have become important tools for assessing risks in such patients. This study aims to evaluate the application value of non-invasive detection indicators in predicting MACE in ACS patients with CKD. Methods The study included 216 ACS patients with CKD， divided into a Non-MACE group （n = 149） and a MACE group （n = 67）. General patient data， non-invasive detection indicators， electrocardiogram （ECG）， and cardiac function indicators were collected. Univariate and multivariate logistic regression analyses were performed to explore the relationship between these indicators and MACE. A nomogram prediction model was constructed， and its performance was evaluated using ROC curve analysis， calibration curve， and decision curve analysis. Results Univariate analysis showed that age， BMI， SVR， SVRI， HRV， QTd， SDNN， LVEF， LVEDd， SCOPA-AUT score， and GRACE score were significantly associated with MACE. Multivariate analysis identified SVRI， QTd， SDNN， LVEF， LVEDd， SCOPA-AUT score， and GRACE score as independent risk factors for MACE. ROC curve analysis revealed that the model had an AUC value of 0.979， sensitivity of 0.925， specificity of 0.966， and accuracy of 0.9537， indicating high diagnostic accuracy. Calibration curve and decision curve analyses further confirmed the model's reliability. Conclusion Non-invasive detection indicators， including SVRI， QTd， SDNN， LVEF， LVEDd， SCOPA-AUT score， and GRACE score， have significant value in predicting MACE in ACS patients with CKD. The constructed prediction model provides an effective tool for clinical risk assessment.