Objective To construct a machine-learning-based model through diagnosis and treatment data of patients with acute ischiic stroke (AIS) to predict healthcare-associated infection (HAI) in AIS patients and provide support for early intervention of clinical treatment. Methods 2360 inpatients with stroke from October 2020 to Deciber 2021 in department of neurology of a tertiary first-class hospital in Jiangxi Province were selected as study subjects and randomly divided at 8 ∶2 ratio into training set (n=1888) and testing set (n=472). Diographic data as well as clinical diagnosis and treatment data within 48 hours of admission were included to analyze the indepen-dent risk factors for HAI in AIS patients. Prediction model of HAI in AIS patients was constructed with logistic regression and three machine-learning algorithms (RandomForest, XGBoost, LightGBM). Area under the receiver operating characteristic curve (AUC) was adopted to evaluate the prediction efficacy of four prediction models. Results HAI occurred in 574 patients, incidence of HAI was 24.32%. Logistic regression analysis showed that age>65 years old, NIHSS score >5 points at admission, white blood cell count >10×10⁹/L, serum sodium ≤135 mmol/L, invasive operation and dysphagia were risk factors for HAI. AUC values of logistic regression, RandomForest, XGBoost and LightGBM prediction models for predicting HAI in AIS patients in the test set were 0.854, 0.850, 0.881 and 0.870 respectively. Conclusion Machine-learning-based model for predicting HAI is conducive to the early identification of HAI and relevant risk factors, and facilitates timely preventive and control measures in AIS patients, thus reduces the incidence of HAI.