Objective To analyze the risk factors for acute kidney injury (AKI) in patients with acute respiratory distress syndrome (ARDS) and to construct logistic regression and decision tree risk prediction models. Methods A total of 160 patients with ARDS admitted to Jiangsu Provincial Hospital of Traditional Chinese Medicine from April 2022 to April 2025 were selected as the research subjects. According to whether AKI occurred concurrently or not, the patients were divided into the occurrence group (n = 51) and the non-occurrence group (n = 109). General data of both groups were collected. Multivariate Logistic regression analysis was used to screen the risk factors for AKI complicating ARDS. SPSS Modeler software was used to construct a decision tree risk prediction model, and its prediction efficiency was analyzed. Results Multivariate Logistic regression analysis showed that age, mechanical ventilation, shock, serum creatinine (Scr), white blood cell count (WBC), and high sensitivity C-reactive protein (hs-CRP) levels were risk factors for AKI complicating ARDS (all P＜0.05). A decision tree risk prediction model was constructed based on the risk factors. The model selected 5 explanatory variables including age, shock, Scr, WBC, and hs-CRP levels, with a total of 4 layers and 13 nodes, among which Scr was the most critical influencing factor. Receiver operator characteristic (ROC) curve analysis showed that the areas under the curve (AUC) of the Logistic regression model and the decision tree model were 0.953 and 0.987, respectively. DeLong test indicated a statistically significant difference in AUC between the two models (Z＝2.467, P＝0.0133). Conclusion Age, mechanical ventilation, shock, Scr, WBC, and hs-CRP levels are independent risk factors for AKI complicating ARDS. The decision tree model constructed based on the above risk factors has high prediction efficiency, which can provide a reference for clinical early identification of high-risk patients with ARDS complicated with AKI and implementation of targeted intervention measures.