Artificial intelligence is increasingly being positioned as an intelligence layer for supply-chain systems exposed to uncertain demand, variable lead times, disruptions, energy constraints, and incomplete information. This paper develops a standalone AI-centered framework in which machine-learning prediction, digital-twin synchronization, anomaly detection, resilience assessment, optimization, and operational authorization are treated as distinct but connected functions. The central contribution is an AI decision-admissibility model: an AI prediction is not interpreted as an executable command unless data quality, twin consistency, predictive confidence, operational feasibility, and authorization conditions are simultaneously satisfied. A state-space representation is introduced for demand, inventory, lead time, capacity, energy availability, and disruption risk. Mathematical constructs are developed for predictive error, anomaly intensity, disruption probability, confidence-aware inference, twin consistency, adaptive recovery, explainability, and an AI resilience index. The framework integrates the supplied literature on digital twins, sustainable logistics, AI, blockchain traceability, smart mobility, batteries, electric vehicles, quantum logistics, and supply-chain resilience. A synthetic evaluation structure is proposed so that comparative performance can be studied without fabricating field measurements. The resulting architecture establishes a pathway from telemetry to prediction, validation, optimization, authorization, execution, and learning while retaining human oversight and auditability.

Ruchita K. Ingole, Gupta A,"AI-Driven Digital Twin Intelligence for Predictive Supply-Chain Resilience and Adaptive Logistics" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(775-780) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.