The widespread adoption of tunnel boring machines (TBMs) has led to an increased focus on disc cutter wear, including both normal and abnormal types, for efficient and safe TBM excavation. However, abnormal wear has yet to be thoroughly investigated, primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear. This study developed a prediction model for abnormal TBM disc cutter wear, considering mixed ground conditions, by employing interpretable machine learning with data augmentation. An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions, and wear data was obtained from 65 cutterhead intervention (CHI) reports covering both mixed ground and hard rock sections. With a balanced training dataset obtained by data augmentation, an extreme gradient boosting (XGB) model delivered acceptable results with an accuracy of 0.94, an F1-score of 0.808, and a recall of 0.8. In addition, the accuracy for each individual disc cutter exhibited low variability. When employing data augmentation, a significant improvement in recall was observed compared to when it was not used, although the difference in accuracy and F1-score was marginal. The subsequent model interpretation revealed the chamber pressure, cutter installation radius, and torque as significant contributors. Specifically, a threshold in chamber pressure was observed, which could induce abnormal wear. The study also explored how elevated values of these influential contributors correlate with abnormal wear. The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters, enhancing the safety and efficiency of TBM operations.