Rate of change, second derivative and volatility of gamma-ray (GR) well-log curves provide useful indicators with which to characterize lithofacies in clastic sedimentary sequences. Rolling averages of these variables, as they change with depth, are also able to distinguish certain lithofacies features. These attributes make it possible to accurately distinguish key facies by using only gamma-ray data, both with formulaic calculations and employing machine-learning (ML) algorithms. This is useful in the many wellbores for which only basic logging suites are available. As well as enhancing lithofacies classification more generally using well-log variables, these GR attributes can be used to forecast facies in real time based on logging-while-drilling data. The application is demonstrated with simple formula using synthetic GR logs featuring common clastic lithofacies and their transitions. Seven widely used ML methods are each trained and validated with a synthetic GR curve (1450 data points) displaying six distinct facies. The ability of the ML model to distinguish those facies using seven GR attributes is compared and further tested with an independent GR data set (800 data points). The random forest algorithm outperforms the other ML models in this facies prediction task, achieving a mean absolute error of 0.25 (on a facies class range of 1 to 6) for the independent testing dataset. The results highlight the benefit of this technique in providing reliable facies analysis based only on GR data. Random forest, support vector classification and eXtreme Gradient Boost are the ML models that provide the most reliable facies classification from the GR attributes defined. Annotated confusion matrices assist in revealing the details of facies class prediction accuracy and precision achieved by the ML and models and classification formulas.