Prediction of elastic mechanical properties such as uniaxial compressive strength (UCS) and static Young’s modulus (E s) is one of the main purposes of studies in geological, geotechnical, geophysical, petroleum, and rock engineering projects. The UCS test is applied to determine them. However, this test is destructive, expensive, time-consuming, and requires high-quality samples. Therefore, using indirect methods seems to be indispensable for estimating dynamic elastic properties. Consequently, the main intention of this study was to predict the relationship between UCS and E s with dynamic poisson ratio (ϑ d) and dynamic Young’s modulus (E d), using simple and multivariate regression analysis (SRA and MRA), an artificial neural network (ANN), and support vector regression (SVR), and to compare and evaluate these methods with each other. For this purpose … Journal Papers