The bridge health monitoring system (BHMS) continuously measures and records the structural responses by using a variety of sensors and communication devices in the bridge operation process. The automatic analysis of signal data can be done effectively in the BHMS to fulfill the timely danger warning and safety assessment. The BHMS leads to the better transportation operation of bridges, the longer service life of bridges, and the more reasonable determination of the bridge management and maintenance for engineers. For the sake of the more efficient application of health monitoring system in bridge engineering, this paper summarizes the current states of several representative BHMS techniques: signal denoising, signal warning, modal parameter identification, finite element model updating, damage identification, condition prediction and assessment. Then, the related researches and applications of these key techniques during 2020 are summarized and discussed. Consequently, it is found that the machine learning methods have been more and more widely used in the current research of key technologies of the bridge health monitoring.