This study conducts a comprehensive review of the promises and challenges of implementing Bayesian statistics in natural hazards engineering. The reviewed natural hazards include earthquakes, floods, extreme wind events, wildfires, and landslides and debris flows. An attributes matrix is developed under each hazard to analyze each study based on its associated scale of analysis, topic area, Bayesian method, and data resource. In particular, the state-of-the-art survey elaborates the level of involvement for three categories of Bayesian methods, such as Bayesian model updating, Bayesian network, and Bayesian neural network, in the topic areas of hazard analysis, risk assessment, and structural health monitoring. In general, the existing research in natural hazards engineering is benefited by leveraging Bayesian statistics to handle uncertainties explicitly and deal with large-scale problems that involve different types of data inputs. However, the substantial computational cost and the determination of prior probability distributions are two major challenges bottlenecking the future development of Bayesian statistics. Compared with machine learning, Bayesian approaches offer more transparent model inference and exhibit different abilities to avoid data over fitting. This reviewed work can serve as a sound reference for interested practitioners and researchers to practice, develop, and promote broader and more in-depth Bayesian advances in solving grand challenges in natural hazards engineering.