This Chapter presents the AI technology and its dynamic role in promoting climate-friendly mobility. It first presents basic AI techniques that can perform tasks requiring human-like intelligence such as reasoning, decision-making, and learning. It then presents examples of specialized AI algorithms and models that are the backbone of artificial intelligence applications as they represent the mathematical and computational frameworks that enable machines to learn, reason, and solve problems. Short presentations are made to general application AI algorithms such as Supervised Learning, Unsupervised Learning, and Reinforcement Learning (RL) as well as to more specific ones such as for Natural Language Processing, Computer Vision, Robotics and Automation, big data handling and data mining. After a brief and concise presentation of the phases of developing AI software systems and platforms, the chapter presents and discusses various AI applications for reducing transport emissions. Of particular interest to the reader may be four specific AI applications that are given as examples from real-life systems and use cases that are presented and analyzed in length . These examples are presented in terms of the current state of technology, the role of AI in reducing carbon emissions, and their potential impacts. Finally, the chapter reviews the most pronounced challenges that exist for the widespread application of AI in climate friendly mobility such as its reliance on large-scale data, the need to construct robust network infrastructures for data transmission and real-time processing, the need for continuous iteration and optimization needed for AI systems, and the substantial computing and energy resources that are necessary.