Aim:To explore the risk factors associated with the occurrence of lower extremity deep vein thrombosis (DVT) after craniotomy in patients with primary brain tumors and develop a predictive model using machine learning. Material and Methods:A prospective cohort study was conducted on 140 patients with primary brain tumors who underwent neurosurgical treatment at our hospital between March 2021 and September 2022. A logistic regression analysis was performed to identify independent risk factors associated with postoperative DVT. Additionally, multiple machine learning models were developed and evaluated to determine their predictive performance. Results:The incidence of lower extremity DVT after craniotomy was 27.9%. Logistic regression identified age [OR=1.07, 95% CI (1.03–1.11)], GCS score [OR=0.88, 95% CI (0.78–0.98)], D-dimer level [OR=1.08, 95% CI (1.02–1.15)], and mechanical ventilation (≥48h) [OR=3.83, 95% CI (1.21–12.15)] as independent risk factors (P<0.05). The Gradient Boosting Machine (GBM) had the highest prediction accuracy among the assessed machine learning models, achieving an area under the curve (AUC) of 0.850, with a sensitivity of 56.44% and a specificity of 90.09%.Conclusion:Age, D-dimer, and mechanical ventilation (≥48h) are independent risk factors for the development of lower extremity DVT after craniotomy in patients with primary brain tumors. The GCS score serves as a potential protective risk factor. The GBM model, with its high AUC and specificity, offers a promising tool for early identification of high-risk patients, potentially informing clinical decision-making and targeted interventions.