This article presents aerodynamic modeling of the fixed-wing aircraft using the Mamdani fuzzy inference network (MFIN). A Mamdani fuzzy inference system with a Gaussian membership function has been used as a nonlinear regression functional node to create a multilayer network, called MFIN. The multilayered MFIN incorporates the nonlinear function approximation capability of the multilayered neural network in addition to robustness against uncertainties and measurement noises. The limited-memory Broyden-Fletcher-Goldfarb- Shanno optimization technique has been used to optimize network parameters to learn the nonlinear yawing moment dynamics of the Advanced Technology Testing Aircraft System (ATTAS) aircraft. Since every node in the network learns the nonlinearity of the dynamics, the proposed MFIN becomes capable of learning highly nonlinear dynamics. The adequacy of the proposed network is validated using the recorded flight data from the ATTAS aircraft of the DLR German Aerospace Centre in two cases: 1) trimmed low-angle-of-attack flight condition and 2) quasi-steady stall high-angle-of-attack (highly nonlinear complex) flight condition. The simulated time history tracking performance, mean square error, R ² score, and explained variance score of the proposed network are compared with state-of-the-art methods. Also, the robustness of the proposed approach is demonstrated by evaluating its performance against test data corrupted with additive white Gaussian noise.