BACKGROUND: The global prevalence of non-alcoholic steatohepatitis (NASH) and its associated risk of adverse outcomes, particularly in patients with advanced liver fibrosis, underscores the importance of early and accurate diagnosis. AIM: To develop a machine learning-based diagnostic model for advanced liver fibrosis in NASH patients. METHODS: A total of 749 patients who underwent liver biopsy at Beijing Ditan Hospital, Capital Medical University, between January 2010 and January 2020 were included. Patients were randomly divided into training ( RESULTS: The Extreme Gradient Boosting (XGBoost) model outperformed all other machine learning models, achieving an AUROC of 0.934 (95%CI: 0.914-0.955) in the training cohort and 0.917 (95%CI: 0.880-0.953) in the validation cohort ( CONCLUSION: The XGBoost model provides a highly accurate, non-invasive diagnosis of advanced liver fibrosis in NASH patients, outperforming traditional methods. An online tool based on this model has been developed to assist clinicians in evaluating the risk of advanced liver fibrosis.