Wind energy is recognized as the fastest-growing sustainable energy source globally. A significant number of wind turbines installed in the past few decades have reached, or are nearing, the end of their life expectancy and must be decommissioned soon. The aging of wind turbines will present major environmental challenges due to the substantial volume of end-of-life materials they are expected to generate. While a good percentage of these materials, such as metals, cement, and electronic components, are theoretically recyclable, practical obstacles hinder the recycling process. One significant obstacle is the labor-intensive process of manually identifying and classifying the various types of materials extracted from decommissioned wind turbines. The development and implementation of digital technologies, such as computer vision and machine learning, can significantly alleviate the challenges associated with manual waste management processes. These technologies can visually identify and categorize materials based on their physical and chemical properties at a significantly faster rate than human labor can achieve. By integrating these technologies, the recycling processes in the wind energy industry will become more efficient, cost-effective, and significantly more environmentally sustainable. This paper presents an automated computer vision system based on deep learning to support the recycling of wind turbines at the end of their lifecycle. The system integrates image recognition, material classification, and optimal processing pathways to identify and classify metals, fiberglass composites, and plastics. Preliminary results demonstrate the algorithm’s potential to significantly reduce efforts in waste processing and enhance the recovery of valuable materials. Our innovative solution promises to contribute to a more sustainable approach to wind turbine decommissioning and support the wind energy sector’s transition to a more circular economy.