Recent advances in machine learning (ML) techniques show promise for estimating soil hydraulic properties from soil datasets. Pedo-transfer functions (PTFs) can facilitate the mapping of the complex relationship between soil properties and soil hydraulic properties, e.g., lateral hydraulic conductivity—a necessity for estimating lateral subsurface flow in distributed hydrological models. In wflow_sbm model, the horizontal-to-vertical saturated hydraulic conductivity ratio (fKh0) is a sensitive parameter, but no established PTF exists. Our objective is to investigate the potential of ML algorithms in estimating PTFs for fKh0 prediction. In this study, publicly available calibrated fKh0 (i.e., optimized) across Great Britain were utilized to train two machine learning algorithms: Random Forest (RF) and Boosted Regression Trees (BRT), employing SoilGrids dataset. Both algorithms effectively predicted fKh0 of 92 sub-basins (out of 115 sub-basins in the 25% test set), demonstrating a high correlation with the optimized values, with RF slightly outperforming BRT. As a next step, we compared wflow_sbm simulated discharge results using uncalibrated fKh0 (default value) and our predicted values. The predictions notably improved discharge simulations, with a median KGE increasing from 0.55 to 0.75. Subsequently, we generated two globally distributed fKh0 maps to investigate the transferability of the ML-based PTFs in the Loire basin, France. ML-based PTFs improved performance in 75% of sub-basins, with an average KGE increase of 0.06. Finally, we assessed the uncertainty in fKh0 predictions, confirming the robustness of the ML-based PTFs. Our study highlights the potential of ML methods for estimating soil hydraulic properties, aiding parameter estimation for distributed hydrological models.