The increasing prevalence of smart homes has opened new avenues for improving energy efficiency by applying IoT technologies and advanced data analytics. This research paper focuses on the development of a comprehensive framework that combines IoT sensors, machine learning models, and automation techniques to monitor, analyze, and optimize energy consumption in residential environments. The system collects data from various household appliances and environmental sensors, providing detailed insights into usage patterns and identifying inefficiencies. Predictive algorithms are employed to forecast energy demand, while rule-based automation adjusts appliance operations to reduce unnecessary consumption. The proposed solution demonstrates that significant energy savings, up to 25% can be achieved without compromising user comfort. In addition to reducing energy bills, the system contributes to sustainability by lowering overall energy waste. Simulation results validate the effectiveness of this approach, showing how IoT-enabled analytics can play a critical role in transitioning toward smarter, more sustainable homes. This study provides a foundation for future innovations in energy management, particularly through the integration of renewable energy sources and more advanced user behavior modeling.