The potential recovery of post-stroke aphasia is highly variable and the rehabilitation outcomes are difficult to predict. This interdisciplinary collaboration builds on data collected as part of a large set of behavioral and brain variables in patients with post-stroke aphasia, charting the course of recovery associated with therapy across language domains and examining the basis of neuroplasticity. In this pilot study, we created and tested a predictive framework based on a subset of the data collected and developed machine-learning algorithms that take as input a complex set of brain and behavioral features to classify and predict the participants' responsiveness to therapy. We developed Random Forest models that enabled us to rank the importance of these features. We then compared the contributions of different feature sets and discussed their physiological implications. Our preliminary results suggest the potential of our framework, and, thus, this study takes an important first step towards predicting individualized rehabilitation outcomes.