With the rapid development of Internet technology, network security issues have become increasingly prominent. Among these, the identification and classification of encrypted traffic have emerged as significant research directions. This paper provides a comprehensive review of current machine learning-based techniques for encrypted traffic classification. First, it briefly introduces common encryption protocols and their characteristics from a layered perspective. Then, it provides an overview of the datasets and evaluation metrics used in this field. Based on this foundation, it discusses both traditional machine learning methods and deep learning methods for encrypted traffic analysis, with a focus on key techniques such as feature engineering and classifier models. Finally, it summarizes the challenges currently faced in this field, including the lack of interpretability and the risk of adversarial examples, and looks ahead to future research directions aimed at enhancing interpretability, automating feature extraction, and optimizing model structures.