Abstract: With the increasing demand for high-precision and high-efficiency machining in advanced manufacturing industries, the dynamic performance of CNC machine tools has become increasingly important to machining accuracy and stability. To address the deficiencies of domestic CNC machine tools in dynamic performance, the research progress on the dynamic characteristics of CNC machine tool process systems was reviewed from the perspectives of overall machine tool dynamics, dynamic characteristics of key components and joint interfaces, and dynamic behaviors of spindle–tool–fixture systems. In addition, the applications of intelligent approaches such as digital twin, deep learning, and data-driven methods in dynamic modeling, online monitoring, chatter state identification, and stability prediction were analyzed. The results show that significant progress has been achieved in machine tool dynamic modeling, spatial dynamic characteristic analysis, nonlinear joint interface modeling, and chatter stability prediction. Representative research approaches, including experimental modal analysis, operational modal analysis, and active/passive chatter suppression, have gradually been established. However, deficiencies still remain in the accurate characterization of dynamic behaviors under complex operating conditions, quantitative analysis of dynamic accuracy, and the integration of mechanism-based and data-driven models. Future research should further strengthen the investigation of dynamic characteristics and dynamic accuracy formation mechanisms of process systems under complex machining conditions, so as to provide theoretical support for improving the machining accuracy and efficiency of CNC machine tools.