Transportation alone accounts for a very high percentage of emissions in the world, which highly contributes to increasing climate change and general pollution. The project explores using green cloud computing to automate resource scheduling and allocation in transportation systems, thereby reducing energy consumption and emissions. This paper proposes a heuristic-based greedy algorithm to dynamically adjust the route, optimize efficiency in routes, avoid idle waste time, and reduce the operational costs of transport vehicles. Our solution makes use of low-carbon technologies, predictive analytics for maintenance, real-time tracking of emissions, or all of these features to enable more sustainability in transportation ecosystems. Experimental results prove that fuel efficiency is higher while emissions are reduced, and the scalability of the system is better.