Although the use of predictive models in rock engineering and engineering geology is an important issue, some simple and multivariate regression techniques traditionally employed in these areas have recently been challenged by the use of fuzzy inference systems and artificial neural networks. The purpose of this study was to construct some predictive models to estimate the uniaxial compressive strength of some clay-bearing rocks, depending on examination of their slake durability indices and clay contents. For this purpose, the simple and nonlinear multivariable regression techniques and the Mamdani fuzzy algorithm are compared in terms of their accuracy. To increase the accuracy of the Mamdani fuzzy inference system, the weighted if-then rules are extracted. To compare the predictive performances of the models, the statistical performance indices (root mean square error and variance account for) are calculated and the results are discussed. The indices reveal that the fuzzy inference system has a slightly higher prediction capacity than the regression models. The basic reason for the higher performance of the fuzzy inference system is the flexibility of the fuzzy approach.