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Autoimmune hepatitis (AIH) is a relatively rare, immune-mediated chronic inflammatory liver disease characterized predominantly by hepatocellular injury. Liver fibrosis assessment is crucial in AIH management. We aimed to develop and validate a novel non-invasive nomogram to predict advanced liver fibrosis in AIH patients. Patients with AIH who had undergone liver biopsy and met the inclusion and exclusion criteria were included in this retrospective study; subsequently, they were randomly assigned to the training set and the validation set in a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation identified predictors from candidate variables. Multivariable logistic regression established independent predictors used to construct the nomogram. Performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA). This study ultimately included 141 patients with AIH (n = 98 for the training set and n = 43 for the validation set). LASSO and multivariate logistic regression analysis showed that liver stiffness measurement (LSM), platelet (PLT), and prothrombin time (PT) were independent risk factors for advanced liver fibrosis in patients with AIH, and a nomogram for diagnosing advanced liver fibrosis was established based on these factors. Calibration was excellent (Hosmer-Lemeshow P > 0.05), and DCA showed significant clinical net benefit. The nomogram demonstrates excellent discriminatory ability: the AUC of the training set is 0.851 (95% CI: 0.777–0.925), and the AUC of the validation set is 0.922 (95% CI: 0.847–0.997). It outperforms single indicators (LSM, PLT, PT), as well as well-known serological models including aspartate aminotransferase to platelet ratio (APRI) and fibrosis-4 (FIB-4). This novel nomogram model has excellent diagnostic performance and can more intuitively and personalizedly assess the probability of advanced liver fibrosis in patients with AIH, thereby potentially reducing the reliance on liver biopsy.