Accurate energy consumption prediction is important for optimizing energy usage, reducing costs, and minimizing environmental impact, particularly in smart homes. This study presents a comprehensive analysis of six leading machine learning algorithms for energy consumption prediction: long short-term memory (LSTM) networks, random forest (RF), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), support vector machines (SVMs), and artificial neural networks (ANNs). The performance of each model was evaluated using metrics such as the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination ( \(\mathrm {R}^2\) ). The findings indicated that the RF model consistently outperformed the other models in terms of prediction accuracy, showing the lowest MAE and MSE, and the highest \(\mathrm {R}^{2}\) , both with and without incorporating weather data. Through examining their performance, strengths, and weaknesses, this study provides valuable insights into the suitability of these algorithms for smart home energy management applications.