Security, luggage tracking, and machine learning models

A method for utilizing a machine learning model for tracking and rerouting passenger luggage, comprising:

• storing, by a cloud-based computing system having at least on processor and memory, a machine learning model;
• interacting, by a local terminal, with the cloud-based computing system and deploying the machine learning model;
• wherein the machine learning model is trained from representative information comprising a plurality of reference indicator data sets from a first message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second message used to train the machine learning model to determine a non-routine route relative to the routine route;
• deploying, by the local terminal, the machine learning model to perform:
• matching first travel information comprising a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a manifest with second travel information from a created message comprising a reference indicator representative of a non-routine routed luggage item;
• retrieving third travel information of the luggage item generated by a baggage handling system correlated to a time frame prior to or after the created message to locate the luggage item;
• and
• generating delivery instructions, based on a baggage journey travel record associated with a master travel manifest for a current travel journey of the luggage item, wherein the delivery instructions include information to reroute the luggage item from its found location to a location of a reservation of a registered passenger.

A method comprising:

• training, deploying, and updating, by at least one processor, a machine learning model with representative information,
• wherein the representative information comprises:
• a plurality of reference indicator data sets obtained from a first message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second message used to train the machine learning model to determine a non-routine route relative to the routine route;
• deploying, by the at least one processor, the machine learning model to perform:
• matching first travel information including a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a flight manifest with second travel information from a created message that includes a reference indicator representative of a non-routine routed luggage item;
• generating for the non-routine routed luggage item associated with the flight manifest;
• retrieving third travel information of the non-routine routed luggage item generated by a baggage handling system prior to or after the created message;
• and
• locating the non-routine routed the luggage item.