Patients with complex coronary artery disease (CAD) often have poor clinical outcomes. This study aimed to develop a predictive model for assessing the 1-year risk of major adverse cardiovascular events (MACE) in patients with stable complex CAD, using retrospective data collected from January 2020 to September 2023 at Guangzhou Red Cross Hospital. The goal was to enable early risk stratification and intervention to improve clinical outcomes. A total of 369 patients were included and randomly divided into a training set (70%) for model development and a validation set (30%) for performance evaluation. Predictive factors were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by logistic regression to construct the model and create a nomogram. Seven independent predictors were identified: functional SYNTAX score (OR 1.257, 95% CI 1.159-1.375), low-density lipoprotein cholesterol (LDL-C, OR 1.487, 95% CI 1.147-1.963, /1mmol/L), left ventricular ejection fraction (LVEF, OR 0.934, 95% CI 0.882-0.985, /1%), albumin (OR 0.889, 95% CI 0.809-0.974, /1g/L), pulse pressure ≥ 72 mmHg (OR 3.358, 95% CI 1.621-7.118), angiotensin-converting enzyme 2 (ACE2) ≥ 27.5 U/L (OR 2.503, 95% CI 1.290-5.014), and diabetes (OR 2.261, 95% CI 1.186-4.397). Among these, the functional SYNTAX score was the strongest predictor. The area under the receiver operating characteristic curve (AUC) was 0.843 for the training set and 0.844 for the validation set, with Youden indices of 0.561 and 0.601, respectively. Calibration curves and decision curve analysis demonstrated good predictive accuracy and clinical utility of the model. These findings suggest that the developed model has strong predictive performance for 1-year MACE risk in patients with complex CAD, and early risk stratification and intervention based on this model may improve clinical outcomes.