Bulk materials constructed from van der Waals 2D heterostructures offer an even larger range of properties than homostructures as they can be arranged in multiple layers consisting of different 2D materials in each layer. They could provide almost infinitely tuneable bespoke materials for almost any application. For example, a recent data mining study reported the existence of 1,825 2D materials that could be exfoliated from known experimental inorganic compounds. This set can make ~1.7 million bilayer structures, around 10 9 trilayers. This quickly becomes an intractable problem for accurate but CPU-intensive computational methods like DFT to explore. Clearly, a method for rapidly predicting the properties of these structures without having to perform many expensive and time-consuming quantum chemical calculations is needed to efficient explore the properties these materials spaces offer. We addressed this by applying several machine learning (ML) models, trained on data from a small number of DFT calculations, to the prediction of the structural and electronic properties of layered van der Waals (vdW) materials, specifically bilayer materials constructed from different 2D materials. The success of the ML approach using a small training set could potentially save a significant amount of experimental and computational time and cost, while retaining acceptable accuracy.