<![CDATA[System optimization has become one of the fundamental pillars of industrial engineering, supporting effective decision-making and improving the performance of complex systems. Over the past century, optimization has evolved from classical mathematical theories into an interdisciplinary framework that integrates operations research, artificial intelligence, machine learning, and advanced computational technologies. This study aims to review the historical evolution, current developments, and future prospects of system optimization in industrial engineering. A narrative literature review was conducted by examining seminal books and peer-reviewed articles indexed in major scientific databases, including Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The reviewed literature was analyzed thematically to identify the progression of optimization methodologies from classical optimization to modern intelligent optimization. The findings indicate that system optimization has shifted from deterministic mathematical modeling toward adaptive and data-driven decision-support systems capable of addressing increasingly complex industrial challenges. Recent advances in artificial intelligence, big data analytics, and quantum computing have significantly expanded the scope and capability of optimization techniques across manufacturing, logistics, healthcare, energy, and smart industrial systems. The review concludes that future research should focus on integrating optimization with emerging digital technologies to develop more intelligent, scalable, and sustainable decision-support frameworks for industrial engineering.]]>
