Mobility as a Service (MaaS), as a part of the smart mobility paradigm, is recognized as one of the most effective solutions for the congestion management (CM) problem in cities. MaaS is a possible sustainable solution for transportation planning, promising the enhancement of traffic management and the lessening of congestion. MaaS can offer travelers access to several modes of transport without the need to own any vehicle, thereby presenting travelers with seamless and carefree traveling. This study aims to develop a methodological frame-work adapting MaaS as a supportive tool to alleviate traffic con-gestion. To support this mobility, the users and the drivers should be connected via a single platform based on an Artificial Intelligence algorithm (Reinforced Learning, for example). Such a strategy would optimize the mobility in the area as a whole over time by learning from actions/decisions such as: ride-sharing matching, taxi dispatching, in-route guiding, and the generation of inter-modal paths. That would help in providing solutions for real-time interaction. Decisions about departure times, paths to follow, and modes of travel would be available for all.