To begin, we create a training database of neutral oxygen vacancy migration activation energies across a diverse structural and chemical space of metal oxides. Our trained model can then screen Materials Project (MP) structures to predict the activation energies of all possible vacancy diffusion paths below a distance cutoff on the path length, and subsequently compute temperaturedependent diffusivity tensors from this data. In addition to generally identifying materials with interesting diffusivity characteristics, utility in a specific materials discovery application is demonstrated via a multi-dimensional down-select of materials for thermochemical water-splitting (TCH) based on predicted oxygen vacancy defect thermodynamics, diffusivity, and host stability. While this dataset considers only neutral oxygen vacancies and does not delve into more complex defect-mediated diffusion pathways (e.g. interstitialfacilitated or knock-out diffusion), we nonetheless expect to identify promising materials that can be investigated on a case-by-case basis with more accurate methods. We therefore also conclude with some perspectives on how the proposed method can be improved with better accuracy and greater applicability for future materials discovery problems.