Abstract: With the continuous advancement of Industry 4.0 and intelligent manufacturing, robotic systems are increasingly applied in industrial automation scenarios, where their motion accuracy and operational reliability are crucial to ensuring production efficiency and operational safety. Compared to traditional mechanical systems, robots are subject to multiple uncertainties and time-dependent structural degradation during complex task execution, leading to distinct time-dependent reliability behavior. This paper provides a comprehensive review of time-dependent reliability in robotic motion, systematically summarizing the current research progress and key technologies in this field. The study first categorizes inherent and epistemic uncertainties in robotic systems and their effects on motion errors. It then outlines typical modeling strategies based on the Denavit–Hartenberg (D-H) method, dynamic modeling, and multi-source error analysis. Furthermore, the paper compares mainstream reliability solution methods—such as numerical simulation, theoretical analysis, and surrogate modeling—in terms of applicability and computational efficiency. Finally, key challenges and future directions in time-dependent reliability research for robotic systems are discussed.