This document discusses using artificial neural networks to forecast the output power performance of a solar thermal lag Stirling engine. Input parameters like angular velocity, temperature, and tank volumes are used to train neural networks. The best performing network structure is selected using metrics like mean squared error and correlation coefficient. Simulation results show the neural network method can accurately predict values like gas temperature, tank volume, and output power in different situations, providing an alternative to complex numerical simulations. This allows quickly assessing the engine's performance without solving governing equations.