Detect and prevent an aircraft instability condition is extremely important, especially for flight control, and morphing airfoils can be used for this purpose. This work proposes the determination of a digital morphing airfoil, using a deep learning approach, to avoid an unstable aeroelastic condition in a 2D wing model. To parametrize the airfoil’s geometry, Bezier – Parsec 3434 parametrization was used and some of the parameters were determined by an optimization process based on a Genetic Algorithm. The airfoil’s geometric Cg position, cl, cd and cm distributions for some angles of attack were used to train a deep neural network, capable to estimate the desired BP3434 parameters. Finally, this trained machine learning model was then coupled to the 2D aeroelastic model of a wing to change the airfoil’s curvature when it faced an instability. The trained deep learning algorithm had an excellent Person’s coefficient of 0.919 when predicting a new geometry. Our methodology permits to automatically detect and avoid instability using digital morphing techniques coupled with AI, using only one sensor, monitoring the dynamic behavior of the airfoil.