<p>Rising energy consumption, driven by industrialisation and urbanisation, contributes significantly to climate change and household economic burdens. In response, this study developed a machine learning model to predict household energy consumption in residential settings. The dataset employed comprises timestamps, temperature, humidity, and weather data. Prior to model training, extensive exploratory data analysis, preprocessing, and feature engineering were conducted to maintain data quality and enhance model performance. After comparing different statistical models, including RandomForestRegressor, ExtraTreesRegressor, SupportVectorRegressor, and XGBRegressor algorithms, ExtraTreesRegressor emerged as the optimal model, with an R² score of 0.7441 and a MAPE of 16.27%. The lower MAPE and the higher R² score indicate the superiority of the ExtraTreeRegressor over other algorithms. While energy consumption is characterised by high variance, our optimised model effectively interprets interactions between input features and predicts the equivalent energy consumed with a lower RMSE of 11.75. This optimised model was integrated into a web application with an interactive user interface. The application programming interface (API) enabled users to make informed decisions about energy consumption, leading to potential energy savings and reduced environmental impact. The feature importance examined for the prediction process of the model revealed the pivotal role of the hour feature in the energy consumption prediction process. Hence, the time of the day defines various occupancy behaviours that could affect energy consumption.</p>