Quantitative prediction of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far-reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate fluid flow, solute transport, and mechanical deformation behavior. They play a central role in many subsurface applications, including CO2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and oil and gas extraction. These applications require the prediction of fracture-dependent quantities of interest such as CO2 leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity. To be useful, these predictions must account for the uncertainty inherent in subsurface systems, such as the number, spatial distribution, and connectivity of existing and induced fractures. Here, we review recent advances in fractured rock research covering field- and laboratory-scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fracture processes and one's ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measures of fracture flow that can be used to identify dominant coupled processes and to validate both conceptual and numerical models. Laboratory-scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics-based simulation of flow and transport provides a bridge in understanding between controlled simple laboratory experiments and the massively complex field-scale fracture systems. Finally, we review the use of machine learning-based emulators and surrogate models to rapidly investigate different fracture property scenarios and accelerate physics-based models by orders of magnitude to enable uncertainty quantification and near real-time analysis.