The convergence of artificial intelligence, advanced materials science, and real-time computing has created transformative opportunities for optimising industrial operations, energy systems, and critical infrastructure. This review synthesises recent advances across five interconnected domains: machine learning-based logistics optimisation, advanced material applications in new energy vehicles, real-time edge AI scheduling for critical infrastructure, climate modelling for building energy resilience, and knowledge graph-based intelligent management systems. The integration of these technologies addresses fundamental challenges in reliability, efficiency, and sustainability across sectors including transportation, manufacturing, energy, and civil infrastructure. We examine how intelligent scheduling algorithms enable low-latency edge AI deployments in autonomous vehicles and smart cities, how advanced materials enhance the performance and longevity of new energy vehicle components, and how physics-informed modelling supports resilient infrastructure design under changing climate conditions. This review further explores the emerging synergy between these domains, identifying opportunities for cross-disciplinary innovation that can accelerate the transition toward intelligent, sustainable industrial ecosystems.
