The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. However, predicting these properties for bimetallic or more complicated surfaces is an open challenge. Computing cleavage energy is the first step in calculating surface energy across a large space. Here, we present a workflow to predict cleavage energies ab initio using high-throughput DFT and a machine learning framework. We calculated the cleavage energy of 3033 intermetallic alloys with combinations of 36 elements and 47 space groups. This high-throughput workflow was used to seed a database of cleavage energies. The database was used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model provides an accurate prediction of cleavage energy with a mean absolute test error of 0.0071 eV/Å2. It can also qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into unexplored chemical space by predicting which surfaces are relatively stable and therefore more realistic. The insights allow us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalyst screening and nanomaterials synthesis.