Artificial intelligence (AI), machine learning (ML) and deep learning (DL) have been rapidly transforming and innovating the transportation sector in recent years. This is not only enabling greener, safer and more efficient mobility solutions, but also combating climate change. This comprehensive study explores the current applications, ethical considerations, advantages and disadvantages of AI, machine learning and deep learning in the implementation of green transportation systems. It also highlights the importance of community involvement in promoting ecology in urbanism. Thus, this study examines ML algorithms such as the Genetic Algorithm (GA), Support Vector Machine (SVL), Naive Bayes (NB), k-means clustering, k-Nearest Neighbor (kNN), Classification and Regression Trees (CART), as well as DL algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), and Autoencoder. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), this review examine the application of AI for green transportation such as Autonomous vehicles, Smart Traffic Management, Mobility-as-a-Service (MaaS), Vehicle-to-Grid technology (V2G), Sustainable Public Transit, Micromobility solutions, and Electric Vehicles (EVs). The findings indicate that while AI technologies offer significant potential for optimizing energy efficiency, reducing emissions and enhancing safety in transport, they also present challenges related to data privacy, algorithmic biases and ethical decision-making. As such, this study highlights the need to develop and implement AI responsibly, reconciling technological advances with ethical considerations and community needs. Finally, this work recommends future research to address these challenges in order to develop more transparent and explainable AI models, and to explore the long-term societal impacts of AI-driven green transportation systems.