pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and reducing healthcare costs. This study aims to develop a predictive model using machine learning techniques, specifically XGBoost combined with SHAP, to identify key risk factors of pressure ulcers in this population. Utilizing the MIMIC-IV 2.2 database, we included a cohort of 29,448 mechanically ventilated patients in ICU intensive unit. These patients were divided into a training set (20,614 patients, 70%) and an internal validation set (8,834 patients, 30%). Of these, 2,052 patients developed pressure injuries. We applied the XGBoost algorithm to build the predictive model and used SHAP analysis to identify the top ten factors influencing pressure ulcer development: 'sepsis', 'age', 'the count of platelet', 'length of ICU stay', 'PaO2/FiO2 ratio', 'hemoglobin concentration', 'admission type', 'renal disease', 'albumin concentration', and 'ethnicity'. The predictive model achieved an area under the ROC curve (AUC) of 0.797 (95% CI: 0.786-0.808) in the training set and 0.739 (95% CI: 0.721-0.758) in the validation set. Calibration curves demonstrated good fit, and the decision curve analysis indicated clinical utility. This study successfully developed a machine learning model that accurately predicts the risk of pressure ulcers in ICU patients with mechanical ventilation. This model could serve as a useful tool for guiding early interventions, ultimately reducing the incidence of pressure injuries in this vulnerable population. The integration of SHAP analysis offers insights into the most critical factors.