aspg-41-3493-1792226226 20261017083706 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Metaheuristic Optimization Review MOR 3066-280X 2025 3 2 Mahmoud Elshabrawy Mohamed Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Forecasting carbon dioxide emissions from vehicles is essential for sustainable transportation, climate-aware urban planning, and intelligent environmental management, as road transport contributes substantially to energy-related greenhouse gas emissions. Rapid growth in vehicle ownership, mobility demand, fuel use, freight activity, and congestion has exposed the limits of conventional emission-estimation methods, particularly when nonlinear and multi-factor relationships must be represented. This review examines how artificial intelligence and machine learning support vehicle carbon dioxide emission forecasting through data-driven models, deep learning, feature selection, hyperparameter optimization, and metaheuristic algorithms. It emphasizes the value of integrating traffic volume, vehicle attributes, fuel type, speed patterns, road conditions, temporal behavior, weather variables, and energy-consumption indicators to improve prediction reliability under real-world conditions. Optimized forecasting models can support emission monitoring, mitigation planning, traffic management, and low-carbon mobility policies, although challenges remain in data availability, interpretability, uncertainty, regional transferability, computational cost, and transparent validation. 2025 21 37 3493 https://creativecommons.org/licenses/by/4.0/ 10.54216/MOR.030205 https://americaspg.com/journal/41/article/3493 https://americaspg.com/storage/articles/manuscripts/01KXBZPJ20J4QJ8DNACHBMMC5G.pdf