The Holy City of Mecca is the place where millions of people gather for religious rituals over the year, and despite the enormous administrative efforts directed to the administration of Congestion Management (CM) during the Hajj and the "Al Mashaaer days", there is always the possibility of introducing better services in subsequent years. This study has a practical outcome in this respect, as it aims to develop a methodological framework that can operate as a supportive tool in the administration of the Hajj, thus easing the movement of pilgrims in congested areas. The methodology acknowledges that the major areas of mobility in Mecca seem to be repetitive, with the leading destination being the Al Kaaba area and particular locations (hotels) being targeted at certain times (Prayer times). 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 mobility in the area over time by learning from actions/decisions such as ridesharing matching, taxi dispatching, en-route guiding, and the generation of intermodal paths. That would help in providing solutions for real-time interaction. Decisions about departure times, paths to follow, modes of travel, and logistic freight movement would be available for all.