The integration of Artificial Neural Networks (ANNs) in product design and lifecycle management within the context of the Circular Economy (CE) framework holds significant potential for enhancing sustainability. This paper presents an ANN-based model designed to optimize product circularity by incorporating diverse data sources, such as Computer-Aided Design (CAD) models, lifecycle performance, and end-of-life information. The proposed model provides a comprehensive and structured approach to managing the data lifecycle, from data collection and preprocessing to analysis and actionable insights generation. By focusing on early-stage design decisions and recovery operations, this approach ensures that all relevant dimensions of product circularity are effectively captured and utilized. The paper highlights the current gaps in the literature regarding data-driven strategies for circular economy implementation at the early stages of product design and discusses the importance of aligning recovery operations with design processes to achieve maximum efficiency. The findings of this study contribute to advancing circular economy principles by leveraging AI technologies for more efficient product lifecycle optimization.