The artificial neural network method was used to obtain the most complete, lacking in open sources information regarding changes in the heat resistance of nickel alloys, allowing for the acquisition of all missing information with a statistically significant amount of initial data. A database containing all known information about 350 grades of heat-resistant nickel alloys has been created for this purpose. The optimal configuration for an artificial neural network has been developed to calculate the missing values of the long-term strength limit. The chosen network training method provided us with the highest prediction accuracy. The database's addition allowed to obtain the dependencies between changes in the heat resistance of nickel alloys and their chemical composition, as well as the temperature-time conditions that simulate operational ones. This information greatly facilitates the comparison of alloy properties. The resulting computing system, which includes a database and a mathematical model of changes in the heat resistance of nickel-based alloys, is able to predict the properties of the developed compositions even before pilot smelting and testing.