Digital Twin (DT) technology offers a transformative pathway for converting spent mushroom substrate (SMS) into sustainable bio-based materials, addressing key challenges in circular manufacturing. This research proposes an integrated framework that combines data acquisition, virtual simulation, and process optimization to convert low-value agricultural waste into high-performance insulation and packaging materials. Leveraging sensor networks and machine learning algorithms, the study presents a pilot implementation using historical data, with full-scale operational validation identified as future work. A preliminary case study with AgriCycle Innovation Ltd demonstrates the framework’s potential to improve energy efficiency, reduce waste disposal costs, and meet insulation performance standards. By addressing research questions on optimizing SMS conversion and enhancing supply chain management, the study offers a scalable and replicable model for agricultural bio-waste valorization. It illustrates how DT systems can support intelligent, adaptive, and resource-efficient manufacturing, advancing the transition toward circular, bio-based production aligned with sustainability and economic goals.