There are frequent tripping faults in the distribution transformation area during the summer peak period. A low-voltage trip prediction model based on a hybrid resampling method and the LightGBM algorithm is proposed. First, an isolation forest is used to eliminate outliers in the samples to solve the problem of data distribution marginalization. Secondly, a mixed resampling method combining NCL under-sampling and SMOTE over-sampling is used to handle the data imbalance of training samples. Thirdly, the LightGBM classifier is trained by the new samples generated by the hybrid resampling algorithm. Finally, the probability of low-voltage tripping faults in the target station area is predicted by the well-trained classifier. The experimental results show that the proposed iF-SMOTE-NCL-LightGBM model achieves the highest performance evaluation indicators, among other prediction models, in low-voltage trip prediction, and can effectively predict low-voltage tripping events. This work is supported by the Science and Technology Project of China Southern Power Grid Co., Ltd. (No. GZJKJXM20170049).