<p>High-Speed Trains (HSTs) play a pivotal role in modern transport systems, which are indispensable for social development and economic progress due to their efficient transport capability. However, with the rapid increase in speed and mileage of HSTs, the research on the safety of HSTs has become an imperative challenge. The bearing is a crucial component for HST, and its integrity largely ensures the normal operation of HSTs and the safety of passengers. Consequently, the research on the monitoring and fault diagnosis of bearings not only avoids HSTs’ accidents but also improves their maintenance mechanism. Accordingly, a survey on fault diagnosis algorithms for HST bearings is conducted. First, this paper gives the bearing distribution in high-speed trains and the reasons for bearing faults, and details the categories of sensors and their corresponding characteristics. Subsequently, we introduce the existing fault diagnosis methods for HST bearings, which are respectively based on signal processing, machine learning, and deep learning, and conduct an in-depth analysis of these methods’ advantages. Finally, the challenges in fault diagnosis of HST bearings are summarized from the aspects of practical application, real-time detection, limited datasets, and noise interference, and some future research works are outlined.</p>