Given the fast pace of urbanization and increasing environmental concerns, it is imperative to transition transportation networks to more sustainable and efficient models. This chapter examines the impact of edge computing on improving the effectiveness and environmental friendliness of green transportation systems. Edge computing, a technology that moves computational capabilities closer to where data is generated, provides notable benefits in terms of processing speed, data protection, and immediate data analysis. Integrating edge computing with artificial intelligence (AI) and Internet of Things (IoT) technology enables transportation systems to achieve enhanced performance in traffic management, energy consumption, and emissions reduction. This chapter explores different implementations of edge computing in green transportation, such as intelligent traffic light management, live vehicle tracking, and proactive maintenance. The analysis also investigates the implementation of edge-enabled electric vehicle (EV) charging stations and autonomous vehicles, emphasizing their capacity to enhance energy efficiency and diminish carbon emissions. Case studies and practical examples demonstrate the concrete advantages and difficulties of incorporating edge computing into both urban and rural transportation networks. Moreover, the chapter explores the collaboration between edge computing and AI-driven analytics to improve decision-making processes for transportation planners and regulators. The text focuses on the technological, economic, and regulatory elements involved in implementing edge computing infrastructure. It also offers valuable insights into upcoming trends and advancements in this industry. The primary objective of this chapter is to offer a thorough comprehension of how edge computing might facilitate the advancement of sustainable and efficient transportation systems, so aiding in the broader objective of creating greener urban settings and reducing the impact of climate change.