Recently, complex manufacturing systems have attracted widespread attention in automobile manufacturing, chip manufacturing, robotics, and other fields due to their advantages of automation, intellectualization, and customizable operation. Modeling and optimization have also become hotspots for research both at home and abroad. In this study, the research background and some typical scenario applications of complex manufacturing systems, such as equipment management, process automation, and scheduling management, are first introduced. Then, modeling and optimization methods and their corresponding practical engineering problems are summarized, especially the application of deep learning, reinforcement learning, and cooperative game in the modeling and optimization of complex manufacturing systems. Finally, the modeling and optimization problems of complex manufacturing systems are summarized, and future research directions on complex manufacturing systems are proposed.