Machine Learning and Circular Bioeconomy Transforming Sustainability Through Intelligent Systems

Bioeconomy refers to managing the flow of materials within nature to build a sustainable economy. The goal is to combine scientific progress with effective policies and frameworks to create systems that are both cost-effective and eco-friendly. Moving towards a circular bioeconomy is essential, as it enables smart systems to handle complexity, variability, and sustainability challenges in bio-based value chains. From precision farming to waste management and closed-loop production, machine learning (ML) plays a vital role by identifying patterns, forecasting trends, and supporting smart decisions. This chapter explores key areas such as supply chain strength, waste handling, predictive upkeep, and lifecycle planning. It focuses on ML tools, digital twin use, and industrial applications to enhance resource flow, improve tracking, and lower environmental impact. Real-world cases show how ML can drive innovation and efficiency in eco-friendly, low-carbon economies.