Artificial Intelligence (AI) and automation hold significant promise for revolutionizing green transportation systems, offering solutions that can enhance efficiency, reduce emissions, and promote sustainable urban mobility. However, the success of these technologies hinges on user acceptance and engagement. Understanding user behavior and acceptance of AI-driven green transportation solutions is crucial for the successful implementation and adoption of these technologies. Despite advancements in AI and automation, there is limited research on how users perceive and engage with these systems. This study addresses this gap by examining the factors influencing user acceptance and behavior towards AI-powered green transportation. Utilizing a mixed-methods approach, the research will collect data through surveys, focus groups, and real-world usage analytics to identify key determinants of user trust, satisfaction, and adoption. The study will explore perceived safety, convenience, environmental impact, and cost-efficiency variables. Additionally, it will investigate demographic differences in acceptance levels and the role of effective communication in fostering user confidence in AI technologies. By understanding these dynamics, the research seeks to develop strategies that enhance user engagement and facilitate the widespread adoption of green transportation systems. The findings will provide valuable insights for policymakers, developers, and stakeholders to design user-centric AI solutions that are not only technologically advanced but also socially accepted and embraced, driving the transition towards sustainable urban mobility.