Anaerobic digestion (AD) process is a well-established biotechnological process that relies on complex microbial communities and engineered systems to convert organic matter into biogas. While the individual roles of operational, biological, and environmental factors in AD performance are well-documented, they are often examined in isolation. This reductionist approach can hinder optimization, effective troubleshooting, and system resilience. This paper introduces a mechanistically linked Seven-Factor AD Performance Model that integrates key variables consisting of Asset Condition (AC), Microbiology (M), Temperature (T), Organic Loading Rate (OLR), Hydraulic Retention Time (HRT), Effective Digester Volume (EDV), and Skilled Manpower (SM). Each factor’s relative influence is assessed using a weighted scoring framework that highlights the dynamic interplay between process inputs and outcomes. Microbiology and Temperature emerge as the most influential factors, while Skilled Manpower is emphasised as the pivotal yet frequently undervalued determinant of the system’s performance. To support sustainable application, the model is coupled with a Governance, Oversight, Support, and Perform (GOSP) approach. Furthermore, an “Accountability Framework†ensures data-driven management, operational continuity, and continuous improvement. Together, this integrated approach serves as a blueprint for both researchers and practitioners to enhance system understanding, operational robustness, and sustainable AD process performance. The Seven-Factor AD Performance Model proposed represents a novel integration of key variables not previously combined in this way and provides a weighted framework that captures the relative influence of key determinants, namely, Microbiology, as the core biological driver; Temperature, which governs microbial activity; Organic Loading Rate, Effective Digester Volume, Asset Condition, and Skilled Manpower, highlighting the interplay between operational inputs, infrastructure, and human expertise; and Hydraulic Retention Time, a secondary but supportive factor shaped by other variables. Acknowledgement will be required for the use of this mechanistically linked Seven-Factor AD Performance Model, as it represents a novel integration of key variables not previously combined in this way. Proper attribution is necessary to recognize the originality of the approach.