Using the energy efficiency dataset, this study explores the application of machine learning (ML) methods in big data analytics for energy efficiency prediction. Along with target variables for heating and cooling energy efficiency, the dataset includes building energy consumption parameters such wall area, roof area, and glazing area. To forecast energy efficiency results, a number of machine learning (ML) methods were used, such as gradient boosting, random forests, support vector machines (SVM), and linear regression. Gradient Boosting obtained the best R2 score of 0.90, according to the data, while Random Forests came in second with an R2 of 0.87. Despite being straightforward, linear regression had an R2 score of 0.74, while SVM had an R2 score of 0.79. Furthermore, Gradient Boosting outperformed other methods in terms of prediction accuracy, especially when dealing with intricate non-linear correlations between energy usage and characteristics. Random Forests demonstrated more stability and resilience to overfitting, particularly with high-dimensional data, but being marginally less accurate than Gradient Boosting. The investigation also highlighted how important feature selection is, showing that adding pertinent features like the buildings’ surface areas and orientation angles greatly benefited Random Forests and Gradient Boosting. According to the results, machine learning models—in particular, ensemble techniques like Random Forests and Gradient Boosting—can greatly enhance energy efficiency forecasts and offer insightful information for building management system energy consumption optimization.