As an important formal tool for describing the time-constrained behavior of real-time systems, timed automata are widely employed in fields such as embedded systems and communication protocols. The traditional way of manually building real-time system models is time-consuming and prone to errors, and automatic inference models have become a research hotspot. This study focuses on the active learning algorithms of time automata, sorts them out according to the data storage structure and equivalent query method, summarizes both the latest research status of active learning algorithms in the current field of time automata, and their core ideas and technical frameworks, with the challenges faced by the current research analyzed at the same time. By comparing the advantages and limitations of various methods, this study hopes to provide researchers with a clear reference framework and propose possible future research ideas, aiming to promote the development of the theory and practice of TA automated modeling.