aspg-18-1632-1792025583 20261015005303 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2021 3 2 Mahmoud Ismail Faculty of computers and Informatics, Zagazig University, Zagazig, 44519, Egypt Shereen Zaki Faculty of computers and Informatics, Zagazig University, Zagazig, 44519, Egypt Heba Rashad Faculty of computers and Informatics, Zagazig University, Zagazig, 44519, Egypt Energy management in smart homes involves the use of technology to optimize energy consumption, reduce waste, and lower energy costs. Smart homes are equipped with various devices, sensors, and systems that are designed to monitor and control energy usage. We proposed a novel Energy Management System (EMS) that integrates Machine Learning (ML) techniques and IoT paradigms to optimize energy consumption and reduce energy costs for sustainable smart homes. In addition to the AI-based EMS, we propose integrating fog computing, a decentralized computing infrastructure, to improve the speed, accuracy, privacy, and security of the EMS. The fog nodes can collect data from the various sensors and devices in the smart home and process the data in real time, reducing latency and allowing for quicker decision-making. By processing data at the edge of the network, fog computing also reduces the amount of data that needs to be sent to the cloud, improving privacy and security. Experimental proof-of-concept simulations demonstrated the efficiency and effectiveness of our system in improving sustainability in smart homes. 2021 85 94 1632 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.030204 https://americaspg.com/journal/18/article/1632 https://americaspg.com/storage/61678881621.pdf