Wind extremes, encompassing both high-intensity wind events and periods of diminished wind activity, pose multifaceted challenges across sectors such as renewable energy production, infrastructure resilience, and environmental risk management. These phenomena, driven by complex interactions within atmospheric systems, demand innovative analytical and predictive approaches. This study explores the application of artificial intelligence (AI) to address these challenges, focusing on its potential to enhance the identification of patterns, improve forecasting accuracy, and integrate diverse meteorological datasets. By leveraging machine learning models and exploring their adaptability to wind-related datasets, this work aims to outline a framework for robust analysis and prediction of wind extremes. The versatility of AI techniques in handling the complexities of wind extremes positions them as pivotal tools for improving preparedness and resilience in various sectors.