Aim To investigate the factors influencing carotid artery calcification in patients with ischemic stroke and to construct a predictive model for columnar plots to provide reference for clinical formulation of prevention and control measures. Methods A total of 500 patients with ischemic stroke were randomly divided into modeling group (350 cases) and verification group (150 cases) according to a ratio of 7∶3, and the incidence of carotid artery calcification was analyzed. LASSO-Logistic regression equation was used to analyze the influencing factors of carotid artery calcification, and the prediction model of carotid artery calcification risk was built. Receiver operating characteristic (ROC) curve and calibration curve were used to analyze the nomogram to predict model differentiation and accuracy. Decision curve analysis (DCA) was drawn to evaluate the validity of the prediction model. Results In the modeling group, compared with those without carotid artery calcification, the age of those with carotid artery calcification increased by 17.87%, the proportion of smoking history increased by 32.69%, the level of fasting blood glucose increased by 22.47%, the level of glycated hemoglobin increased by 0.69%, and the level of low density lipoprotein cholesterol (LDLC) increased by 17.84%, the uric acid level increased by 22.42%, the high sensitivity C-reactive protein (hs-CRP) level increased by 40.31%, and the estimated glomerular filtration rate (eGFR) level decreased by 7.04%, with statistical significance (P＜0.05). In the verification group, compared with those without carotid artery calcification, the age of those with carotid artery calcification increased by 17.23%, the proportion of smoking history increased by 33.39%, the level of fasting blood glucose increased by 22.37%, the level of glycated hemoglobin increased by 0.75%, the level of LDLC increased by 17.96%, and the level of uric acid increased by 24.44%, hs-CRP level increased by 30.81%, eGFR level decreased by 6.46%, the difference was statistically significant (P＜0.05). In the modeling group and the verification group, the prediction models of carotid artery calcification were constructed based on the above factors, and the AUC for predicting carotid artery calcification was 0.953 and 0.972, respectively, which was accurate and clinically effective. Conclusion Increasing age, smoking history and increased fasting blood glucose, glycated hemoglobin, LDLC, uric acid and hs-CRP levels are independent risk factors for the occurrence of carotid artery calcification, and elevated eGFR levels are independent protective factor. The prediction model based on the above factors has certain predictive value for the occurrence of carotid artery calcification.