Ti-48Al-2Cr-2Nb (TiAl-4822) materials fabricated via electron beam melting (EBM) additive manufacturing exhibit pronounced variations in fatigue life under complex service conditions, which affects their engineering reliability. To this end, on the basis of fatigue test data consisting of 103 EBM-fabricated TiAl-4822 samples, a high-accuracy fatigue life prediction model (overall error <20%) was developed by combining data augmentation techniques (SMOTE, SMOGN) with machine learning methods (hierarchical neural network, abbreviated as HNN, and Huber regression). The model first employs SMOTE to balance and argument the dataset and then integrates an HNN classifier to determine whether a sample would pass the fatigue test, achieving a classification accuracy of 80%. For the samples that failed the fatigue test, SMOGN  was applied for data augmentation, and a two-stage model combining Huber regression with the HNN was used for fatigue life prediction, leading to an R2 of 0.81 and a mean absolute percentage error of 7.3%. SHAP analysis based on this model indicates that frequency, maximum stress, temperature, and stress amplitude are the primary influencing factors. A fatigue life prediction approach suitable for small-sample scenarios of EBM TiAl-4822 under service conditions is finally established.