A precise prediction of household energy usage is critical for optimizing energy management, forecasting demand, and formulating sustainable energy policies. In this study, we explored three feature selection techniques: LASSO, Random Forest feature importance, and Mutual Information Regression to determine the key factors that impact household energy consumption. After we identified the most relevant features which is Random Forest feature importance, we proceeded to assess the performance of eight distinct machine learning models. These models encompassed: Generalized Additive Model (GAM), Least Angle Regression (LARS), Huber Regressor, Random Sample Consensus Regressor (RANSAC), Extreme Gradient Boosting (XGBoost), Partial Least Squares Regression (PLS), Extremely Randomized Trees (ExtraTrees), and Light Gradient Boosting Machine (LightGBM). Our findings demonstrated that the ExtraTrees regressor surpassed all other models, achieving an outstanding accuracy of 99.99% on the test set. This demonstrates the effectiveness of feature selection and machine learning model in capturing the intricate connections between the input characteristics and the intended variable. The feature selection analysis uncovered the important factors. These valuable insights provide valuable guidance to policymakers and energy service providers as they work toward developing targeted interventions and customized energy efficiency programs. This study highlights the success of a thorough feature engineering and model selection approach in accurately predicting household energy consumption. The findings of this research have important implications for the development of smart energy management systems and the promotion of energy-efficient behaviors at the household level.