The inappropriate sorting of construction and demolition waste (CDW) has severe environmental and economic consequences, including inefficient resource utilization and missed opportunities for recycling valuable materials. While the separation of large CDW particles is generally effective, sorting the fine fraction, also known as screening residue, remains a challenge. Improving the sorting of fines is crucial for promoting a circular economy, as they often contain materials with high recycling potential. This study proposes an intelligent recycling system based on artificial intelligence (AI) to address this challenge. The system combines advanced technologies such as sensors, cameras, and robotics to improve the recycling rate of the CDW fine fraction. AI systems utilize machine learning algorithms to accurately identify and classify materials based on their physical, chemical, or optical characteristics. A major advantage of AI is its ability to continuously learn and adapt. This is particularly beneficial for dealing with the diverse and ever-changing nature of CDW, where waste composition can vary significantly. Data collection is a key element in developing these systems; however, it can be a time-consuming process. Therefore, improving the availability and quality of open-source databases is essential to facilitate the deployment of these modern sorting systems.