Energy, environment, and economy are driving forces behind adopting circular economy principles in industrial production, particularly in the sheet metal industry, where resource optimization and waste reduction are prioritized. Remanufacturing, especially in automotive manufacturing, offers significant material reuse and conservation opportunities. However, challenges persist in developing effective product designs and remanufacturing methods for material recovery. Artificial intelligence (AI), machine learning, and big data analytics present promising solutions to these obstacles, facilitating sustainable practices in metal product remanufacturing. Predictive models, such as those for incremental sheet forming (ISF), streamline the assessment of remanufacturing feasibility, reducing costs and time associated with technical complexities. Integration of AI enables forecasting potential failures and optimizing remanufacturing processes, resulting in considerable savings in effort, energy, and time while advancing circular economy initiatives. In this study, a series of ISF experiments was conducted using a computer numerically controlled machine to redeform a contoured part. Subsequently, using an experimental dataset, an experimental dataset, three AI classifier models—artificial neural network, random forest, and support vector machine (SVM)—were developed to assess the feasibility of remanufacturing a predeformed sheet metal part. SVM emerged as the top-performing algorithm, demonstrating robust classification capabilities. The present study focuses on a limited parameter set that overlooks other influential factors such as the previous manufacturing processes, material composition, and sheet thickness. Expanding the scope to incorporate these factors will enable more accurate assessments and optimized manufacturing processes, ultimately benefiting the remanufacturing industry.