Artificial intelligence (AI) methods have been used widely in power transformer fault diagnosis with notable developments in solutions for big data problems. Training data is essential to accurately train AI models. The volume, scope and variety of data samples contribute significantly to the success and reliability of diagnostic outcomes. This paper provides a comprehensive review and comparison of different augmentation methods used to generate reliable data samples for minority and majority classes to balance the diversity and distribution of dissolved gas analysis (DGA) datasets. The augmentation method presented in this paper combines three common AI models—the Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbour (KNN)—to assess performance for diagnostic fault determination and classification, with comparator assessment using no data augmentation. Comparative analysis of the hybrid models uses evaluation metrics including accuracy, precision, recall, specificity, F-score, G-mean, and the area under receiver operation characteristic (Auc). Experimental results presented in this paper confirm that the data augmentation applied to AI models can resolve difficulties in imbalanced data distribution and provide significant improvements for fault diagnosis, particularly for minority classes.