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In power transformers, accurate diagnosis prediction plays a crucial role in ensuring the reliability and efficiency of electrical systems. This paper presents an enhanced diagnostic prediction approach for power transformers by combining the Synthetic Minority Over-sampling Technique (SMOTE) and Deep Neural Networks (DNN). Our approach addresses the challenge of imbalanced data in power transformer diagnosis prediction by leveraging SMOTE to generate synthetic samples and balance the dataset. By effectively handling rare events through the augmentation of minority class instances, our predictive model becomes more robust. Additionally, using deep neural networks enables the exploration of complex patterns and relationships within the transformer dataset. The DNN architecture integrates multiple layers of learnable features, enabling the prediction model to extract high-level representations and achieve improved accuracy and performance. To evaluate the effectiveness of our proposed approach, we conducted extensive experiments using real-world power transformer datasets. Comparative analysis of the results demonstrates that the combination of SMOTE and DNN outperforms traditional classification techniques applied to imbalanced datasets. The proposed model achieves a higher training accuracy of 98.22 % and a testing accuracy of 94.6 % and mitigates the risks associated with misdiagnosis, contributing to power systems' overall reliability and maintenance efficiency. [ABSTRACT FROM AUTHOR]