Urban transportation is a significant contributor to global carbon emissions, exacerbating climate change and urban air quality issues. This study explores how Artificial Intelligence (AI)-driven transportation systems can mitigate these environmental impacts by optimizing routes, reducing congestion, and enhancing public transportation efficiency. Through a combination of empirical data analysis, cities cases, and simulation models, this research evaluates the effectiveness of AI applications in urban mobility. The findings demonstrate that AI-driven optimizations lead to substantial reductions in carbon emissions, improved energy efficiency, and better utilization of existing infrastructure. Additionally, the study addresses the challenges and barriers to implementing AI solutions in urban settings, providing recommendations for policymakers and urban planners to foster sustainable transportation ecosystems. This research contributes to the growing body of knowledge on sustainable urban development and highlights the pivotal role of AI in achieving environmental sustainability goals.