Incorporating domain knowledge into machine learning techniques for materials design improves predictive capability on small size datasets. Here we propose a hierarchical machine learning (HML) approach to predict bulk mechanical properties. A small library of 18 unique thermoplastic polyurethanes (TPUs) were synthesized to have varying chemical structures, hard segment weight fractions, and functional group indices. The bottom layer of the model is populated in terms of monomer chemical structure, molecular weights, functional indices, and weight fractions. A middle layer, parameterized in terms of the bottom layer descriptors, captures underlying physical properties by incorporating thermodynamic relationships utilizing Group Interaction Modeling (GIM) and measurable experimental values such as surface contact angle (θC), morphology, and quantum descriptors. The domain heavy middle layer is utilized to predict the Young's Modulus for various TPUs which were compared to a test set of various molecular architectures not seen in the training set.