System and method for dynamically scheduling API-based shipment updates across carriers

A shipping management system comprising:

• a memory configured with:
• shipping records representing shipments from a plurality of carriers;
• and
• a plurality of scheduling rules respectively corresponding to the plurality of carriers, wherein a first scheduling rule of the plurality of scheduling rules specifies a number of application programming interface (API) calls allowed over a given time period for a first carrier of the plurality of carriers, wherein a second scheduling rule of the plurality of scheduling rules specifies scheduling based on whether information returned by a second carrier of the plurality of carriers comprises a complete shipping history with each update, and wherein the first scheduling rule of the plurality of scheduling rules is prioritized over the second scheduling rule of the plurality of scheduling rules, and wherein the plurality of scheduling rules are configured to limit the update of the API calls to within respective rate limits of the plurality of APIs;
• a processor coupled to the memory;
• and
• a non-transitory computer readable medium comprising a set of computer executable instructions, the computer executable instructions executable by the processor to:
• dynamically schedule, according to the plurality of scheduling rules and accounting for the first scheduling rule being prioritized over the second scheduling rule, updates to update the shipping records;
• based on the dynamic scheduling, issue update API calls using a plurality of APIs respectively corresponding to the plurality of carriers to request shipment data for the shipments, such as to optimize use of computer and network resources;
• using a machine learning model to analyze and interpret the shipment data returned in response to the update API calls;
• and
• update the shipping records based on the shipment data that was analyzed and interpreted by the machine learning model.