Accurate prediction of CO2 emissions from vehicles is essential for environmental regulation and sustain-able transport design. Existing models often suffer from limited accuracy due to suboptimal hyperparameter configurations. T h is s t udy a i ms t o e n hance C O 2 e m ission f o recasting b y c o mbining d e ep l e arning 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 Algo-rithm (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 pro-posed FbOA-EALSTM framework offers a scalable, accurate solution for emissions modeling and supports data-driven environmental policy and intelligent vehicle technologies.