Optimizing CO2 Emission Forecasting from Vehicles Using Deep Learning and Football Optimization Algorithm Omnia M. Osama 1 , El-Sayed M. El-Rabaie 2 , Marwa M. Eid 3,4 1 Department of Communications & Electronics,Delta Higher Institute of Engineering & Technology , Mansoura, Egypt. 2 Faculty of Electronic Engineering , Menoufia University , Department of Electronics and Communications , Menouf 32952, Egypt . 3 Faculty of Artificial Intelligence , Delta University for Science and Technology , Mansoura 35111, Egypt. 4 Jadara Research Center , Jadara University , Irbid 21110, Jordan. Emails: Omnya.osama@dhiet.edu.eg , srabie1@yahoo.com , mmm@ieee.org Abstract 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 study aims to enhance CO2 emission forecasting by combining deep learning 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. Keywords: CO2 Emissions , Metaheuristic Optimization ,Football Optimization Algorithm (FbOA), Encoder LSTM (EALSTM) ,Transportation Sustainability