The unanimous focus of the sustainable technological development is energy conservation and environmental friendly production. Power management is an essential aspect of sustainable development. It not only support energy production and conservation, but also increases the life time of domestic appliances and thereby reducing the global electronic wastage. The existing systems involving Artificial Intelligence (AI) were mere prediction models, without the evidence on the detailing behind the prediction. Traditional AI systems have focused on predictive analysis but often lack transparency in decision-making and limiting consumer trust. This study proposes a solution combining remote power monitoring with the ZigBee module and Explainable Artificial Intelligence (XAI) to offer both predictive accuracy and interpretability. XAI models are more consumer oriented in every area of application, similar to the problem discussed, which tells about the impact of various parameters in power management in domestic appliances. Local Interpretable Model Agonistic Explainer(LIME) and SHAP explainer are used in the proposed work, providing explainability in the local and global surrogates. The proposed work applies various regression models such as Decision Tree (DT), Random Forest(RF), Support Vector Regressor (SVR), Gradient Boost Regressor (GBR) and Extreme Graident Boost Regressor (XGBR). The RF provides the best R2-Score of 94.71% , which is 1.5%–3.0% more than the rest of the models, and also with variance score of 68.82% , had been chosen for explainability. This study demonstrates how XAI can improve transparency and reliability in AI-powered domestic energy systems, offering actionable insights for more sustainable power consumption.