The integration of machine learning algorithms, advanced material engineering, and real-time task scheduling constitutes a transformative force in contemporary industrial systems. This paper synthesizes recent research across logistics network optimization, new energy vehicle component enhancement, and edge artificial intelligence scheduling for critical infrastructure. The review systematically examines algorithmic approaches to supply chain efficiency, material innovations for automotive performance, reliability modelling for vehicle parts, and latency-aware scheduling strategies for multi-core edge platforms. Knowledge graph applications in industrial auditing and multi-objective optimization for transnational manufacturing are also explored. Findings indicate that cross-domain integration yields superior system performance, heightened reliability, and improved operational efficiency, suggesting a unified framework for future intelligent system design.
