With the deep integration of cloud computing, edge computing and the Internet of Things (IoT) technologies, smart manufacturing systems are undergoing profound changes. Over the past ten years, an extensive body of research on cloud-edge-end systems has been generated. However, challenges such as heterogeneous data fusion, real-time processing and system optimization still exist, and there is a lack of systematic review studies. In this paper, we review a cloud-edge-end collaborative sensing-communication-computing-control ( ${\mathrm{S}\mathrm{C}}^3$) system. This system integrates four layers of sensing, communication, computing and control to address the complex challenges of real-time decision making, resource scheduling and system optimization. The paper combs through the key implementation methods of intelligent sensing, data preprocessing, task offloading and resource allocation in this system, and analyzes their advantages and disadvantages. On this basis, feasible methods for overall system optimization are further explored. Finally, the paper summarizes the main challenges facing the deep integration of cloud-edge-end and proposes prospective research directions, providing a structured knowledge base and development framework for subsequent research. The paper aims to stimulate further exploration of multilevel collaborative mechanisms for smart manufacturing systems to enhance the real-time decision-making and overall performance of the smart manufacturing system.