This chapter first highlights the importance of cycle time prediction by mentioning the management activities supported by cycle time prediction results. Existing cycle time prediction methods are then divided into six categories: statistical methods, production simulation, machine learning or deep learning methods, case-based reasoning, fuzzy modeling methods, and hybrid methods. For each type of methods, the mathematical background behind it is first explained, and then numerical examples and program codes are provided. In recent years, some advanced information technologies have emerged that can be used to enhance the performance of cycle time prediction: Industry 4.0, big data analysis, edge computing, cloud computing and ubiquitous computing, explainable artificial intelligence, etc. How these advanced information technologies cope with the challenges faced by traditional cycle time prediction methods, and some applications of these advanced information technologies in cycle time prediction are introduced. Numerical examples and program codes are given as well.