Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/4123 2022 2022 Department of Communications & Electronics,Delta Higher Institute of Engineering & Technology , Mansoura, Egypt Omnia Omnia Faculty of Electronic Engineering , Menoufia University , Department of Electronics and Communications , Menouf 32952, Egypt El-Sayed M. El El-Rabaie Faculty of Artificial Intelligence , Delta University for Science and Technology , Mansoura 35111, Egypt; Jadara Research Center , Jadara University , Irbid 21110, Jordan Marwa M. Eid Accurate prediction of CO2 emissions from vehicles is essential for environmental regulation and sustainable transport design. Existing models often suffer from limited accuracy due to suboptimal hyperparameter configurations. This s tudy a ims t o e nhance C O2 e mission f orecasting b y c ombining d eep l earning with advanced metaheuristic optimization. An attention-based Encoder LSTM (EALSTM) model is trained on Canadian vehicle emissions data, with hyperparameters tuned using a novel 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 EALSTM model yields an RMSE of 0.00349, MAE of 0.00010, and R2 of 0.984, outperforming all alternatives. These results demonstrate the effectiveness of domain-inspired metaheuristics in improving prediction accuracy. The proposed FbOA-EALSTM framework offers a scalable, accurate solution for emissions modeling and supports data-driven environmental policy and intelligent vehicle technologies. 2025 2025 32 51 10.54216/JAIM.100203 https://www.americaspg.com/articleinfo/28/show/4123