Planning and optimizing completion design for hydraulic fracturing require a quantifiable understanding of the spatial distribution of the brittleness of the rock and other geomechanical properties. Eventually, the goal is to maximize the stimulated reservoir volume with minimal cost overhead. The compressional and shear velocities ( and , respectively) can also be used to calculate Young’s modulus, Poisson’s ratio, and other mechanical properties. In the field, sonic logs are not commonly acquired and operators often resort to regression to predict synthetic sonic logs. We have compared several machine learning regression techniques for their predictive ability to generate synthetic sonic ( and ) and a brittleness indicator, namely hardness, using the laboratory core data. We used techniques such as multilinear regression (MLR), least absolute shrinkage and selection operator regression, support vector regression, random forest (RF), gradient boosting (GB), and alternating conditional expectation. We found that the commonly used MLR is suboptimal with less-than-satisfactory predictive accuracies. Other techniques, particularly RF and GB, have greater predictive capabilities. We also used Gaussian process simulation for uncertainty quantification because it provides uncertainty estimates on the predicted values for a wide range of inputs. Random forest and extreme GB techniques also show low uncertainties in prediction.