This section aims to enhance energy efficiency in IoT-based smart home applications, addressing a significant gap in the literature and demonstrating the feasibility of various energy optimization-based solutions. Using the Kaggle dataset Appliances Energy Prediction, algorithms such as Gray Wolf Optimization, Genetic Algorithm, and Particle Swarm Optimization—methods not previously applied to this dataset to the best of our knowledge—contribute significantly to reducing energy consumption while maintaining user comfort. The findings reveal that managing IoT devices efficiently benefits both environmental and economic aspects. For future studies, more comprehensive analyses of energy management in smart homes can be conducted by utilizing different datasets and optimization algorithms, particularly multi-objective optimization techniques. Additionally, predictive analyses can be developed through refined and enhanced machine learning and deep learning methods, offering broader perspectives on energy management solutions.