Abstract: A system and method (referred to as a system) schedules vehicles transporting freight on a mesh network using a machine learning process. The system receives transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements. The system mines large data sets from remote sites that reflect distances between the loading locations and the destination locations and corresponding freight rates associated with distances through the machine learning process. The system predicts shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules. The system matches the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.