To improve the accuracy of online fault prediction for traction transformers, an online fault diagnosis method based on random forest feature optimization and improved honey badger optimization algorithm is proposed. Firstly, the SMOTE algorithm is used for data balancing processing, and the uncoded ratio method is adopted to expand the fault diagnosis; secondly, the feature vector set is ranked by importance using RF, and then input into the Extreme Learning Machine, Support Vector Machine, and Long Short Term Memory Neural Network to obtain the optimal combination of the base model and the number of features; then, the honey badger optimization algorithm was improved by combining Tent chaotic mapping strategy, improved control factor, and pinhole imaging strategy, and compared with other optimization algorithms to demonstrate its effectiveness in optimization ability, stability, and convergence speed; finally, by combining the improved honey badger optimization algorithm with the optimal base model and number of features, the problem of hyperparameter setting in the base model was effectively solved. The experimental results show that the fault diagnosis accuracy of the proposed online fault prediction model for traction transformers is 96.05%, which is 2.44% higher than that of HBA-LSTM, verifying the effectiveness of the proposed method.