In recent years, artificial intelligence (AI) methods have prominently proven their use in solving complex problems. Across science and engineering disciplines, the data-driven approach has become the fourth and newest paradigm. It is the burgeoning of findable, accessible, interoperable, and reusable (FAIR) data generated by the first three paradigms of experiment, theory, and simulation that has enabled the application of AI methods for the scientific discovery and engineering of compounds and materials. Here, we introduce a recipe for a data-driven strategy to speed up the virtual screening of two-dimensional (2D) materials and to accelerate the discovery of new candidates with targeted physical and chemical properties. As a proof of concept, we generate new 2D candidate materials covering an extremely large compositional space, downselect 316,505 likely stable 2D materials, and predict the key physical properties of these new 2D candidates. Finally, we hone in on the most propitious candidates of functional 2D materials for energy conversion and storage.