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The longitudinal wave velocity (v L) in a rock bolt is a useful parameter for evaluating the conditions of the rock bolt and the surrounding rock mass. This study investigated the influence of rock properties on the prediction of v L in a rock bolt using numerical and machine learning approaches. Through numerical simulations, we obtained a dataset of the variations in v L according to rock properties, compressional wave velocity (v p), shear wave velocity (v s), density (ρ), Poisson's ratio (μ), porosity (η), uniaxial compressive strength (UCS), and slake durability index (I SD). This dataset was used to design a deep neural network, and the predicted v L was correlated with rock properties. Notably, v L is strongly correlated with v p , v s , ρ , η , UCS , and I SD. Principal component analysis was employed to characterize the relationship between the rock properties, and the retaining rock properties for random forest (RF) were determined. In the RF, the variable importance (VI) of rock properties was assessed. In particular, v s emerged as the most significant predictor of v L. However, relying on v s to predict v L is not sufficient because it accounts for approximately 60–70 % of the VI. For a more reliable prediction of v L , it is essential to incorporate both v s and v p , which collectively account for approximately 80 % of VI. Notably, the VI of physical properties (v p , v s , ρ , and η) accounts for more than 90 %, implying that these properties can be effectively used to predict v L even in the absence of data concerning mechanical properties. [ABSTRACT FROM AUTHOR]