Dissolved gas analysis has emerged as a fundamental technique for diagnosing faults in transformers over the past several decades. However, conventional interpretation criteria often yield ambiguous or inconclusive results when applied to the same dataset, primarily due to the subjective nature of defining the boundaries of each method. To mitigate this limitation and enhance diagnostic precision, artificial intelligence (AI) methodologies have been developed in recent years. This paper proposes the utilization of deep neural networks (DNN) to assess transformer faults using a comprehensive DGA dataset. Additionally, three common AI models-support vector machine (SVM), k-nearest neighbors (KNN), and decision tree models were rendered to provide a comparison with the proposed DNN approach. The effectiveness and reliability of the DNN model are underscored by the comparative accuracy, surpassing 98%, marking a significant improvement. The robust performance and superiority of the proposed method are therefore validated in providing dependable diagnoses for transformer faults.