Nickel-based superalloys are unique high-temperature materials with complex doping, used, in particular, in gas-turbine engines. These materials exhibit excellent resistance to mechanical and chemical degradation. The main service property of the alloy is its heat resistance, which is expressed, in particular, by the ultimate tensile strength (UTS). When determining the service life of a superalloy product, the developers investigate only certain combinations of temperature parameters and exposure time. The availability of data on the properties of alloys over the entire range of temperatures and time exposures would greatly expand the possibilities of alloys application and would allow more accurate assessment and comparison of alloys. We applied the Bayesian regularized artificial neural network to simulate the missing UTS values for more than 300 well-known superalloys. Network input parameters are the chemical composition and tensile test conditions. Special data pre-processing and a developed learning algorithm significantly reduced the model prediction error. Comparison of the predicted and experimental data showed excellent convergence. A model check was performed on a test data set (10 alloys), which was combined from samples that were not involved in network training.