The convergence of machine learning, advanced materials engineering, and real-time scheduling algorithms represents a transformative paradigm in modern industrial and edge computing systems. This paper synthesizes recent advances across these interconnected domains, examining how intelligent optimization algorithms enhance logistics networks, how novel material applications improve new energy vehicle performance, and how sophisticated scheduling frameworks enable efficient edge AI deployment. Through comprehensive analysis of contemporary research, this study identifies synergistic opportunities and critical challenges in integrating these technologies for next-generation cyber-physical systems. The findings demonstrate that cross-domain integration, particularly through machine learning-driven optimization and real-time task scheduling, yields substantial improvements in system efficiency, reliability, and sustainability.
