The Stirling engine is a kind of external combustion engine that is ideal for converting renewable energies to electricity or mechanical energy forms. Therefore, monitoring and controlling some of its parameters such as its speed is important. The purpose of this paper is to control a Stirling engine flywheel rotation speed using an intelligent parameter predictor. Since various parameters affect motor performance, determining the optimal value of them manually is impossible. Therefore, linking these parameters together and finding a relationship between them would be beneficial. Hence, using artificial intelligence (AI) to find a quick and efficient solution is particularly important. At the studied Stirling engine, speed, cold sink and ambient temperatures are defined as input parameters, and hot sink temperature is considered as the output parameter that should be calculated. To discover the relationship between inputs and output, an artificial neural network (ANN) is used. The results of this study showed that the use of ANNs can be significantly helpful in controlling engine speed.