Researchers have developed and validated a prognostic model to predict the risk of acute respiratory distress syndrome (ARDS) in critically ill patients with intra-abdominal sepsis using machine learning. Data were obtained from the MIMIC-IV and eICU-CRD databases, and external validation was performed on patients in the First Affiliated Hospital of Xinjiang Medical University from 2016 to 2024. The model includes 14 predictors, such as mechanical ventilation, vasoactive drugs, chronic lung disease, SOFA score, GCS score, vital signs, and laboratory values. The stacked ensemble model achieved an AUC of 0.811 in the development cohort, 0.794 in the internal validation cohort, and 0.756 in the external validation cohort. SHAP analysis showed that mechanical ventilation is the most influential predictor and early vasoactive drugs reduce the risk of ARDS. The model is stable, interpretable and serves on a web calculator to support clinical decisions.