aspg-18-2277-1792025542 20261015005222 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2024 11 1 Fausto Vizcaino Naranjo Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes, Ecuador Silvio Machuca Vivar Docente de la Universidad Regional Autónoma de los Andes, Ecuador Edmundo Jalon Arias Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes, Ecuador Reem Atassi Higher Colleges of Technology, United Arab Emirates, UAE In the era of smart cities, the pursuit of sustainability stands as a paramount goal, with energy management playing a central role. This paper is dedicated to the exploration of early energy consumption prediction as a linchpin in the realization of sustainable smart cities. Employing advanced long short-term memory (LSTM) networks, we introduce a potent predictive model tailored to anticipate energy consumption patterns within urban environments. Notably, our model achieves remarkable performance metrics, with a root mean square error of 547.71 and a strikingly low mean absolute percentage error (MAPE) of 1.22. Through meticulous comparisons against baseline models, our LSTM-based approach emerges as a beacon of accuracy, reliability, and sustainability. Beyond predictive analytics, our research offers actionable insights for urban planners and policymakers, fostering the creation of greener, more sustainable, and ecologically responsible smart cities that harmonize technological innovation with environmental stewardship. As smart cities continue to evolve, our work lays the foundation for a future where sustainability is not merely a goal but a reality. 2024 12 20 2277 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.110102 https://americaspg.com/journal/18/article/2277 https://americaspg.com/storage/11701534034.pdf