Apparatus and method of multi-point transportation

A method of using an apparatus for multi-point transportation, comprising:

• receiving, by at least a processor, transport data of at least a transport from a transportation entity;
• receiving, by the at least a processor, a plurality of constraints to verify a compliance of reception of the transport data by the at least a processor, wherein receiving the plurality of constraints comprises receiving data related to previous iterations of processing transport data stored, by the at least processor, in an immutable sequential listing;
• verifying, by the at least a processor, the transport data through the immutable sequential listing, wherein the transport data comprises a sub-listing, the sub-listing comprising a cryptographic hash;
• generating, by the at least a processor, weighted bound parameters as a function of the transport data and an optimization criterion wherein the weighted bound parameters indicate relative importance of a particular bound parameter;
• categorizing, by the at least a processor, the at least a transport into a transport subgroup as a function of weighted bound parameters;
• receiving, by the at least a processor, training data correlating transport data inputs to transport statuses outputs;
• iteratively training, by the at least a processor, a transport status machine learning model with the training data wherein iteratively training the transport status machine learning model comprising:
• applying a weighted value to an input layer of nodes comprising the transport data inputs, an intermediate layer of nodes, and an output layer of nodes comprising the transport statuses outputs;
• and
• adjusting one or more connections and the one or more weights between nodes in adjacent layers of the transport status machine learning model;
• retraining the transport status machine learning model as a function of the adjusted one or more connections and the adjusted one or more weighted between nodes in adjacent layers;
• outputting, by the at least a processor, using the trained transport status machine learning model, a transport status of the transport subgroup;
• communicating, by the at least a processor, the transport data of the transport subgroup to at least a transport entity;
• updating, by the at least a processor, the transport status of the transport subgroup as a function of the communication;
• and
• displaying, by the at least a processor, the updated transport status of the transport subgroup to a user through a graphical user interface (GUI).