Engineers constantly seek advancements in the performance of aircraft and power generation engines, including, lower costs and emissions, and improved fuel efficiency. Nickel-base superalloys are the material of choice for turbine discs, which experience some of the highest temperatures and stresses in the engine. Engine performance is proportional to operating temperatures. Consequently, the high-temperature capabilities of disc materials limit the performance of gas-turbine engines. Therefore, any improvements to engine performance necessitate improved alloy performance. In order to take advantage of improvements in high-temperature capabilities through tailoring of alloy microstructure, the overall objectives of this work were to establish relationships between alloy processing and microstructure, and between microstructure and mechanical properties. In addition, the project aimed to demonstrate the applicability of neural network modeling to the field of Ni-base disc alloy development and behavior. A full program of heat-treatment, microstructural quantification, mechanical testing, and neural network modeling was successfully applied to next generation Ni-base disc alloys. Mechanical testing included hot tensile, hot hardness, creep deformation, creep crack growth, and fatigue crack growth. From this work the mechanisms of processing-structure and structure-property relationships were studied. Further, testing results were used to demonstrate the applicability of machine-learning techniques to the development and optimization of this family of superalloys.