This article provides a thorough review of the advancements in acoustic emission (AE) technology used for monitoring journal bearings. First, the AE sources generated from journal bearings under different lubrication regimes are classified and discussed. Next, a comparative analysis of parametric analysis, waveform, and artificial intelligence recognition methods for bearing AE signal analysis is conducted, highlighting their respective principles, pros and cons, and applications. Additionally, an overview of physical models representing AE waves on relatively sliding surfaces is provided from the wave generation mechanism perspective, and each model’s applicable conditions are compared. Finally, an in-depth discussion is presented, and future research directions are highlighted.