Stress-strain curves are important representations of a given material's mechanical properties, which depend primarily on the orientation of the individual crystals in the microstructure. Generating stress-strain curves from numerical methods such as the crystal plasticity finite element (CPFE) simulations is computationally intensive. As a result, it is difficult to generate complete stress-strain curves for all possible orientations of a material. In this work, we propose a bilinear stress-strain curve prediction framework for metallic alloys by integrating supervised and unsupervised deep learning methods via transfer learning principles. As a specific case-study, we focus on predicting stress-strain curves of Nickel (Ni)-based superalloys that have important applications in aerospace industry. Using a small training set of just 100 complete stress-strain curves (4,000 strain steps each) of different orientations generated by CPFE simulation code, we were able to build a model that could accurately predict stress-strain curves (