Here we present a framework for leveraging QTAIM descriptors as general, robust features for geometric machine learning tasks. Our framework extends to both molecular and reaction-level predictive tasks and thus can be applicable in a wide-set of use cases. We created tools for both high-throughput calculation of QTAIM descriptors and a custom machine learning package for easily implementing models that use these features.