The Multi-Modal Transportation (MMT) problems are one of the most complex and most modern problems that will remain to be in focus for coming years, and it seems like these AI technologies are going to help us significantly. The review of research provided training programmes in MMT, useful applications, strategies for implementation as well as AI and the prospects associated with it together with future challenges. Based on the detailed investigation of relevant papers and a critical examination, this research provides insights about various roles for which AI is applied via meta-analysis to discover common themes. It shows AI as a revelation in the service of efficient transport systems, operational optimization and enhanced user experience which is crucial. In AI, predictions in demand forecasting and route optimization to real-time decision-making support systems as well as automotive autonomous technologies exists for other applications. Action plans cover implementation of AI algorithms in already deployed systems, development of AI-driven connected mobility platforms and PPP strategies. However, remaining issues around data privacy, interoperability of vehicles and infrastructure investment both monetarily into the technology as well as social acceptance continue to exist. These range from enabling the complete AI landscape for personalized mobility services, leveraging 5G and edge computing new generation technologies to co-creating with an ecosystem of stakeholders. This overview does justice to the critical role expected for AI in MMT, and provides some insights into how it stands at present—as much as challenging areas needing further exploration.