Efficient energy management for household appliances is crucial for maximizing domestic energy use and facilitating proactive maintenance. Recent researches indicate that traditional forecasting approaches frequently lack the precision and real-time capabilities essential for effective household energy management. This paper provides a holistic method that combines IoT data gathering, machine learning (ML) and Explainable AI (XAI) to improve the precision and comprehensibility of energy consumption forecasts in residential buildings. Among the 18 models that were assessed, polynomial regression was identified as the most efficient choice for this particular assignment. Non-invasive sensors are used to capture data effortlessly, and real-time monitoring is accomplished through a web application built on Flask, with remote access facilitated by Ngrok. The predictive model, augmented by Physics-Informed Neural Networks (PINNs), ensures that predictions are consistent with physical laws. The system’s performance is rigorously evaluated using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²), achieving superior results with an RMSE of 0.03, MAE of 0.02, MAPE of 0.04, and an R² of 0.9989. XAI techniques, including SHAP and LIME, are employed to explain the model’s predictions, providing insights into the factors influencing household energy consumption. This research establishes a new benchmark for residential energy management, offering a scalable, interpretable, and highly accurate solution for real-time energy optimization and predictive maintenance in household environments.