In the pursuit of environmental sustainability and cleaner transportation, the global automotive industry is expediting transformation. This paper utilized multi-decade data spanning from 1975 to 2024, for the development of predictive models for fuel economy and CO₂ emissions across a wide range of vehicle technologies from 2026 - 2050. This is done with the help of advanced machine learning algorithms like Linear and Random Forest Regression in Python and integrating insights through Power BI visualizations, the project identifies key correlations between vehicle attributes such as weight, powertrain, and footprint and their environmental performance. Results highlight the increasing impact of electric vehicle adoption, hybridization, and light weighting on overall emissions reduction. These insights help forecast the direction of fuel economy standards, emission patterns, and technology shifts across manufacturers and vehicle types. Beyond technical predictions, the study offers a decision-support framework for global policymakers, automotive designers, and sustainability advocates. The findings provide the importance of data-driven approaches that can increase regulatory compliance, influence the innovation process, and support sustainable mobility solutions on a global scale.