AI for Multimodal Transport Optimization: Integrating Air, Sea, and Land Logistics for Efficiency

Abstract

The combination of air transportation with sea routes and land systems forms vital components in global supply chain operations but their operational value deteriorates when management is disorganized and operational challenges occur. The research evaluates how Artificial Intelligence (AI) boosts multimodal logistics operations by developing better decisions and cutting costs and strengthening delivery dependability. The deployment of predictive models and optimization algorithms functions through a simulated examination of U.S.-based logistics organization Trans Co. Throughout a twelve-month period, the implementation of Python-based tools resulted in a 20% reduction of transit expenses together with a 15% increase in delivery punctuality and 10% lower carbon footprint. The achieved results demonstrate how AI transforms operations while supporting the key U.S. objectives for building sustainable resilient supply chains. The final part of the document provides functional recommendations alongside future scopes specifically designed to embrace blockchain technology for improved logistics network transparency and scalability.