aspg-28-4123-1792155166 20261016125246 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 10 2 Omnia M. Osama Department of Communications & Electronics, Delta Higher Institute of Engineering & Technology, Mansoura, Egypt El-Sayed M. El-Rabaie Faculty of Electronic Engineering, Menoufia University, Department of Electronics and Communications, Menouf 32952, Egypt Marwa M. Eid Faculty of Artificial Intelligence , Delta University for Science and Technology , Mansoura 35111, Egypt Jadara Research Center , Jadara University , Irbid 21110, Jordan Accurate prediction of CO2 emissions from vehicles is essential for environmental regulation and sustainable transport design. Existing models often suffer from limited accuracy because of suboptimal hyperparameter configurations. This study enhances CO2 emission forecasting by combining deep learning with metaheuristic optimization. An attention-based Encoder LSTM (EALSTM) model is trained on Canadian vehicle-emissions data, with hyperparameters tuned using the Football Optimization Algorithm (FbOA), inspired by cooperative team dynamics in football. Comparative evaluation against eight other optimizers shows that FbOA achieves the best performance. The optimized FbOA–EALSTM model yields an RMSE of 0.00349, MAE of 0.00010, and R2 of 0.984, outperforming the alternatives reported in the study. These results demonstrate the potential of domain-inspired metaheuristics to improve prediction accuracy. The proposed framework offers a scalable solution for emissions modeling and supports data-driven environmental policy and intelligent vehicle technologies. 2025 15 26 4123 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.100203 https://www.americaspg.com/journal/28/article/4123 https://www.americaspg.com/storage/articles/manuscripts/01M4J55VXCVWPPFZ5XWA83KWA1.pdf