Interconnected planning, warehouse, and fleet systems have turned cyber exposure into a first-order supply-chain risk rather than an after-hours information-technology problem. This reconstructed review unifies machinelearning detectors with business-intelligence analysis inside a four-layer architecture that also admits living twins, ledger commitments, spectral residue monitors, and traction-pack state. Prior work uses learning extensively for threat detection while business-intelligence practice remains fragmented, and joint frameworks for small firms and developing corridors are scarce. Integration is defined at data, process, and decision levels so that predictions feed dashboards and dashboards write policy back into models. The paper expands each layer with working identities, working identities, scattered graphs, and a scored agenda that treats cybersecurity, freight resilience, and electric mobility as one adaptive continuum.

Gupta A. A,"Coupling Machine Learning and Business Intelligence for Cyber-Resilient Supply Networks and Adaptive Mobility" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(482-490) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.