Automation and artificial intelligence (AI) are becoming important in the development of green transportation systems.This abstract investigates the varied role of AI-driven automation in improving the efficiency, sustainability, and safety of transportation systems. AI application in green transportation includes a variety of technologies such as self-driving cars, smart traffic control systems, predictive maintenance, and energy-efficient logistics. Autonomous cars, outfitted with powerful AI algorithms, are at the vanguard of this transformation, promising to minimize carbon emissions by optimizing driving patterns and energy usage. These cars use machine learning models to read real-time data from sensors, cameras, and Global Positioning System (GPS), allowing them to make more informed judgments that save fuel and minimize traffic congestion. The decrease of human error also helps to improve safety and reduce accident rates. Smart traffic management systems (TMS) utilize artificial intelligence to monitor and analyze traffic flow, reducing idle and emissions. These systems use data from road sensors and linked vehicles to provide a comprehensive view of urban traffic, promoting greener transportation networks. AI-driven automation also plays a crucial role in energy-efficient logistics, optimizing route planning, load allocation, and delivery scheduling to save fuel and reduce greenhouse gas emissions. AI also facilitates the integration of electric vehicles (EVs) into logistics fleets, improving the sustainability of goods transportation. The integration of automation and AI into green transportation systems offers a significant opportunity to improve sustainability and efficiency, reducing the transportation industry's environmental footprint and promoting sustainable growth. The continued progress and use of AI in this sector are crucial for achieving the full potential of green transportation systems.