Global carbon dioxide emissions from fossil fuels and industry reached a record in 2022, emphasizing the importance of reducing pollution from the automotive sector. Vehicles represent a significant source of CO₂ emissions, and large fleets are a major challenge in managing the carbon footprint. Although there are strict regulations and initiatives to reduce emissions, companies that manage large fleets face difficulties in collecting and reporting accurate data on vehicle emissions. The lack of uniformity in the provision of data by car manufacturers and the difficulties associated with manual retrieval of information from vehicle documents complicate this process. To address these challenges and with the aim of selecting the best algorithm to integrate into a decision support system, the following machine learning algorithms were involved in the research: Random Forest, XGBoost, and a Feed-Forward Neural Network, to predict CO₂ emissions based on vehicle characteristics from a fleet of over 26,500 records. The models were trained using numerical and categorical data, properly preprocessed, providing a good solution for emissions prediction in the absence of real measured data for newly ordered vehicles. Based on these findings, all of them can be integrated into a complex decision support system, enabling more effective and informed decision-making. The results indicate that the Random Forest algorithm is the most effective for predicting CO2 emissions in this context, as it captures complex relationships between features and the target variable delivering accurate predictions. In this context, the use of machine learning models for large vehicle fleets not only ensures CO2 emission predictions but also contributes to sustainable fleet management and compliance with environmental regulations.