<p>In recent years, companies have needed a new paradigm of economy for sustainable development to defend the environmental and sustainable values of society. The circular economy can serve as an effective tool to reduce environmental impacts and help organizations achieve better performance. Recently, several studies on the circular economy have been published, but few have examined the performance of manufacturing industries in terms of circular economy measures. The aim of this paper is to provide a discriminant evaluation of the circular economy performance of manufacturing industries. By reviewing the literature and interviewing experts, fourteen features of the circular economy were identified. Additionally, Student’s t-test was used to select the features. In this study, the performance of manufacturing industries in adopting circular economy practices was analyzed, which should be distinguished from general sustainability, as circular economy focuses on resource optimization, waste reduction, recycling, and closed-loop production. The features selected for analysis included hiring and training of environmental staff, employees with specialized environmental expertise, volume of recycled green space waste, volume of recycled paper and cardboard waste, volume of recycled glass waste, green energy consumption, electricity consumption, green purchases, raw materials consumed by parent companies, and raw materials purchased domestically. To group industries based on these features, the k-means algorithm was used, clustering similar industries together, and discriminant analysis was applied to validate the clusters and develop a predictive function for classifying industries into clusters based on their feature values. Cluster analysis revealed two distinct groups: Cluster1 performed well in gas consumption, green energy consumption, and investment in environmentally friendly technologies but below average in other features, while Cluster2 showed strong performance in domestic raw materials consumption and gas consumption, but underperformed in other areas. This study demonstrates that machine learning algorithms can identify performance patterns in circular economy practices and provide managers with insights to formulate policies for improving resource use and recycling, while future research could leverage deep learning algorithms to achieve higher accuracy and deeper insights.</p>