The Circular Economy (CE) offers a regenerative alternative to the traditional linear economic model by emphasizing resource efficiency, waste minimization, and lifecycle extension. However, selecting the most effective circular initiative among hundreds of possibilities remains a major challenge for organizations seeking sustainable transformation. We present CED-AI, an intelligent extension of our Circular Economy Database that integrates a machine-learning recommendation engine to guide decision-makers in prioritizing high-impact CE actions. CED-AI combines a feature-rich dataset—spanning cost metrics, environmental impact scores, technology readiness levels, and organizational feasibility—with a supervised learning model benchmarked on a labeled dataset of 50 circular case examples. The system computes an Initiative Priority Score for each entry and delivers ranked suggestions through a static prototype interface with scenario-based views. In a cross-validation evaluation, CED-AI achieved 92% top-5 precision, significantly reducing manual analysis time. A user study with industry practitioners further validated its usability and decision-support value. By embedding explainable AI modules that surface key feature contributions, CED-AI not only accelerates sustainable strategy planning but also maintains transparency essential for stakeholder trust. This work demonstrates how AI-powered tools can transform circular-economy adoption, offering a scalable approach to embed data-driven intelligence into sustainability frameworks.