Abstract: This paper investigates the application of various machine learning models for predicting the health index and remaining lifespan of power transformers. We evaluate the performance of individual models including Random Forest, Support Vector Regression (SVR), Gradient Boosting, and Elastic Net in estimating the health index of power transformers. Additionally, a stacking ensemble approach is implemented by combining the predictions of these individual models.