Angioimmunoblastic T-cell lymphoma (AITL) is a life-threatening hematological malignancy. For patients with poor prognosis, especially those with expected survival less than 1 year, the benefits from traditional regimens are extremely limited. Therefore, we aimed to develop an interpretable machine learning (ML)-based model to predict the 1-year overall survival (OS) of AITL patients. A total of 223 patients with AITL treated in 4 centers in China were included. Five ML algorithms were built to predict 1-year outcome based on 16 baseline characteristics. Recursive feature elimination (RFE) method was used to filter for the most important features. The ML models were interpreted and the relevance of the selected features was determined using the Shapley additive explanations (SHAP) method and the local interpretable model-agnostic explanation (LIME) algorithm. Catboost model presented to be the best predictive model (AUC = 0.8277). After RFE screening, 8 variables demonstrated the best performance (AUC = 0.8125). This study demonstrated that the Catboost model with 8 variables could effectively predict the 1-year overall survival of AITL patients.© 2025. The Author(s).