This article introduced a smart energy managementEnergy management strategy that uses two complementary machine learningMachine learning (ML) algorithms to predict and optimize energy consumption patterns in smart environmentsEnvironment. A recurrent neural networkRecurrent Neural Network (RNN) (RNN) is used to model time-seriesTime-series prediction energy data, while an ensemble method (such as XGBoostXGBoost) handles decision-making for energy control actions. A smart energy managementEnergy management strategy is proposed that leverages two complementary machine learningMachine learning algorithms to predict energy consumption and optimize system control. The system is designed to be deployed on low-power embedded devices, enabling local real-time control. Unlike conventional rule-based or static models, our approach incorporates predictive uncertainty and dynamic adaptation, enabling deployment on low-power embedded systemsEmbedded systems or edge devices. The suggested architecture combines a recurrent neural network (RNNRecurrent Neural Network (RNN)) for time series forecasting and a tree-based ensemble method (Random Forest or XGBoostXGBoost) for real-time decision support. The results obtained on publicly available datasets show improved accuracy, faster adaptation, and energy savings compared to baseline single-model approaches. Finally, numerical results demonstrate improved performance over single-model systems in accuracy, robustness, and computational efficiency.