Objective To explore the independent risk factors for aortic valve calcification （AVC） in patients with coronary artery calcification （CAC） and construct a clinical predictive model， thereby providing a basis for the early identification of high-risk patients. Methods This study retrospectively incorporated 1458 inpatients who underwent coronary CT angiography （CCTA） at Zhongda Hospital Affiliated to Southeast University from January 2019 to September 2022 and had a CAC score greater than 0. These patients were randomly allocated into a training set （n = 1 020） and a validation set （n = 438） at a ratio of 7：3. The risk factors of calcific aortic valve disease （CAVD） were analyzed via univariate and multivariate logistic regression. In this research， LASSO regression was employed for variable screening， and a nomogram prediction model was developed based on the screened variables. The excellent discriminatory capacity， prediction precision， and potential clinical application value of the model were verified through receiver-operating characteristic （ROC） curve， calibration curve， and decision curve analysis （DCA）， respectively. Results The incidence of AVC in the training set was 30.6%. Multivariate analysis indicated that age （OR = 1.055， 95% CI： 1.040 ~ 1.071）， height （OR = 0.980， 95% CI： 0.962 ~ 0.997）， right ventricular end-diastolic diameter （RVEDd， OR = 1.719， 95% CI： 1.102 ~ 2.692）， and statin use （OR = 1.408， 95% CI： 1.047 ~ 1.899） were independent predictors of AVC. The area under the curve （AUC） of the model in the training set and the validation set were 0.738 and 0.715， respectively. The calibration curve demonstrated that the predicted risk was highly consistent with the actual risk， and DCA verified that its clinical net benefit was significant. Conclusions Age， height， RVEDd and statin use are independent predictors of AVC in patients with CAC. The nomogram model constructed based on these factors demonstrates excellent predictive ability， which is conducive to the early identification and clinical management of high-risk patients.