###### Abstract  
The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized linear model (GLM) to enhance both predictive accuracy and uncertainty quantification in Vs prediction. The study utilizes an Extreme Gradient Boosting (XGBoost) algorithm coupled with Shapley Additive Explanations (SHAPs) and partial dependency analysis to identify key geotechnical parameters influencing Vs predictions. Additionally, a Bayesian GLM is developed to explicitly account for uncertainties arising from geotechnical variability. The effectiveness and predictive performance of the proposed models were validated through comparison with real case scenarios. The results highlight the unique advantages of each model. The XGBoost model demonstrates good predictive performance, achieving high coefficient of determination (\(R^{2}\)), index of agreement (IA), Kling-Gupta efficiency (KGE) values, and low error values while effectively explaining the impact of input parameters on Vs. In contrast, the Bayesian GLM provides probabilistic predictions with 95% credible intervals, capturing the uncertainty associated with the predictions. The integration of these two approaches creates a comprehensive framework that combines the strengths of high-accuracy ML predictions with the uncertainty quantification of Bayesian inference. This hybrid methodology offers a powerful and interpretable tool for Vs prediction, providing engineers with the confidence to make informed decisions. s 2025 15 140  
CPT parameters (cone tip resistance qc and sleeve friction fs). Many notable examples of such correlations can be found in the literature (e.g., see the works of [PERSON] and others [11; 12; 13]). However, these empirical models often exhibit limitations in generalization, as they are based on site-specific data and are unable to capture the complex, non-linear relationships inherent in the soil properties influencing Vs. Regardless of the techniques, there is unavoidable uncertainty in the final Vs profile, which contributes to uncertainty in site response predictions [14]. Accounting for Vs uncertainty is of paramount importance in engineering applications. While there has been a great deal of research on the significance of incorporating uncertainty of Vs into engineering practices, no universally accepted methods exist for realistically addressing this critical issue [14]. [PERSON] and [PERSON] [15] highlighted the use of bounding-type profiles, which is developing upper and lower bounds (e.g., \(\pm\)20%) around a base Vs profile to represent potential variability. Additionally, statistical approaches, such as Monte Carlo simulations and the [PERSON] [16] Vs randomization model, have been utilized to generate a range of Vs profiles that reflect the inherent uncertainty in subsurface conditions. With the increasing availability of high-quality, freely accessible data (e.g., CPT data), the use of machine learning (ML) algorithms for soil characterization has become increasingly popular in recent years [17; 18]. Research related to Vs prediction using various algorithms shows promising results. Notable examples include predictions of Vs from CPT parameters (e.g., see authors' previous research and many others) [19; 20; 21; 22]. ML algorithms excel at learning complex relationships within data without relying on the prior knowledge of the underlying mathematical models. This capability makes them highly effective for analyzing highly variable geotechnical data. However, the development of ML models must be approached with caution, as their performances heavily depend on several factors, including data preprocessing, feature selection, and hyperparameter tuning.