Because of their superior efficiency, stability, and ability to produce maximum power under various typical operating situations, wind turbines driving doubly fed induction generator systems are frequently utilized in wind power extraction. These systems face stability problems especially at severe faulty conditions. This paper focuses on the fault ride-through of a doubly fed induction generator (DFIG) driven by a wind turbine using Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed ANFIS technique detects the faulty conditions from the measured voltages and currents at the terminals of the generator. In case of faulty cases, an ANFIS technology activates the wind turbine's pitch angle controller and the crowbar resistance. Wind turbine's pitch angle controller protects the rotating parts of the system from over speeding when the fault occurs to ensure that the generator does not deviate from stability by adjusting the aerodynamic torque of the wind turbine. Crowbar resistance protects electrical parts of the system, especially DC bus voltage and power electronics converters. A comparison between the behavior of DFIG at faulty conditions without any fault controller and with the proposed ANFIS technique is applied. When the ANFIS technique is used, the wind system's performance and response are improved. The proposed ANFIS control system has proven its effectiveness in protecting the DFIG in the event of a grid fault.