Conventional black-box models fail to provide the insights needed to optimize the complex process of anaerobic digestion. We address this by introducing a novel interpretable AI framework that pairs real-time IoT data with a Temporal Fusion Transformer (TFT). Based on a pilot-scale digester, this study serves as a proof-of-concept, demonstrating that the TFT framework can achieve high predictive accuracy while uncovering key variables governing methane production. Our work provides a methodology for enabling genuine process understanding, laying the groundwork for advancing the circular bioeconomy through intelligent, datadriven control.