A new approach, based on a Generalized Regression Neural Network (GRNN), has been proposed to predict the unsteady forces and moments of two different models; a 70\degree swept delta wing in subsonic incompressible flow and a standard fighter model (SDM) in a compressible flow regime, both undergoing sinusoidal pitching motion. Extensive wind tunnel results were used for training the network and verification of the values predicted by this approach. GRNN was trained by the aforementioned experimental data and, subsequently, was used as a prediction tool to determine the unsteady longitudinal forces and moment of the two models under various conditions. Further, it was applied to extend the experimental data beyond the conditions tested in the tunnel. The results are in a good agreement with the experimental findings. This indicates that the present prediction and optimization tool provides sufficient accuracy with a modest amount of experimental data.