The creation of intelligent, sustainable energy management solutions is required due to the sharp rise in the world's energy demand and growing environmental concerns. The incorporation of Internet of Things (IoT) technologies provides a revolutionary method for tracking, managing, and optimizing energy usage in the commercial, residential, and industrial domains. In order to provide real-time energy monitoring and adaptive control, this study offers a thorough IoT-based framework that combines smart sensors, wireless communication protocols, cloud computing, and embedded analytics. Energy waste is reduced and operational efficiency is increased by using machine learning models to estimate energy demand and optimize resource allocation. Significant benefits are shown through experimental evaluation, including a decrease in peak load demand, improved forecasting accuracy (MAPE and RMSE improvement), and a decrease in overall energy use. The suggested approach promotes data-driven decision-making, scalability, and interoperability, establishing IoT as a crucial facilitator for the development of sustainable infrastructure, smart grids, and renewable energy integration. This work contributes to efficient energy use and environmental sustainability by laying the groundwork for future developments in intelligent energy systems.
 

Internet of Things,Sustainable Energy Management,Smart Grid,Energy Optimization,Machine Learning