Purpose: To deeply analyze the influencing factors after surgery in patients with glioma and establish a more accurate prognostic model to provide a reliable basis for clinical treatment decisions and prognostic evaluation. Methods: Retrospectively collect the clinical data of a large number of glioma patients, including information on age, gender, tumor location, pathological grade, degree of surgical resection, postoperative radiotherapy, chemotherapy and other aspects. Use advanced statistical analysis methods to conduct univariate and multivariate analyses to determine the key independent risk factors affecting postoperative survival of patients. Based on these factors, use powerful machine learning algorithms to establish a prognostic model and comprehensively evaluate the model through internal and external validations. Results: Comprehensive analysis shows that factors such as age, pathological grade, degree of surgical resection, postoperative radiotherapy, and chemotherapy are significantly related to the postoperative survival time of patients. Multivariate analysis further reveals that pathological grade, degree of surgical resection, and postoperative radiotherapy are the core independent risk factors affecting postoperative survival of patients. The established prognostic model has excellent predictive performance and can accurately evaluate the prognosis of patients. Conclusion: This study clarifies that pathological grade, degree of surgical resection, and postoperative radiotherapy are important influencing factors for postoperative survival of glioma patients. The established prognostic model provides a powerful prognostic evaluation tool for clinicians, helping to formulate personalized treatment plans and improve the survival rate and quality of life of patients.