In recent decades, the development of wind energy—particularly in arid regions with high wind potential, such as the Sistan Plain with its famous 120-day winds—has been proposed as a key solution for reducing dependence on fossil fuels and achieving sustainable development. However, sustainable operation of wind turbines in this climate faces challenges including climatic fluctuations, component erosion, premature failures, and efficiency losses, which reduce the overall system reliability. This study presents a hybrid approach based on Failure Mode and Effects Analysis (FMEA) and artificial intelligence models to enhance the performance and reliability of wind turbines in the arid climate of Sistan. In the first stage, historical turbine performance data—including output power, temperature, wind speed, downtime duration, and failure events—were collected and preprocessed. Subsequently, based on Severity, Occurrence, and Detection indices, the FMEA risk matrix was developed and critical components were identified. In the second step, the risk data were used as inputs to learning models, including Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to predict failure probability and optimize turbine performance parameters. The proposed hybrid model, by integrating statistical and intelligent data features, is capable of detecting failure trends before occurrence and providing predictive maintenance strategies. Experimental results demonstrated that the FMEA–ANN model reduced the failure rate of critical turbine components by 23% and increased the overall energy conversion efficiency by 11%.