P. Sujatha, A. Amala, Uday Kumar Burugu, Gopavarapu Indrasena Reddy,Vamshi Krishna Balla 02 07 Transformer failures represent a significant threat to the stability and reliability of electrical power systems often resulting in unexpected outages costly maintenance and prolonged downtimes Proactive and accurate classification of potential transformer faults is critical for minimizing operational disruptions and enabling efficient maintenance scheduling This study introduces an ensemble machine learning framework aimed at improving the prediction accuracy and reliability of transformer failure classification The existing system utilizes a Decision Tree Classifier due to its interpretability and ease of implementation However it suffers from overfitting and limited generalization especially when exposed to complex or noisy datasets To address these challenges a Random Forest Classifier is proposed leveraging ensemble learning by combining the outputs of multiple decision trees This approach enhances model robustness effectively reduces variance and improves the handling of nonlinear feature interactions Comparative analysis using standard performance metricsincluding accuracy precision recall and F1scorereveals that the Random Forest model consistently outperforms the Decision Tree across all metrics The proposed model demonstrates a more reliable and scalable solution for intelligent fault diagnosis in the power grid Overall this project emphasizes the importance of ensemblebased machine learning in critical infrastructure applications offering a practical pathway toward smarter and more resilient transformer monitoring systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026