Anaerobic Digestion (AD) technology emerges as a viable solution for managing municipal organic waste, offering pollution reduction and the generation of biogas and fertilisers. This study reviews the research works for the advancements in AD implementation to effectively impact the UN Sustainable Development Goals (SDGs). Furthermore, the study critically analyses responsible waste management that contributes to health and safety, elevating quality of life in both rural and urban areas and, finally, creates a map of AD outputs onto all 17 SDGs. Finally, the assessment employs the three sustainability pillars (i.e., economic, environmental, and social perspectives) to examine the direct and indirect links between AD and all 17 UN SDGs. The findings reveal substantial progress, such as poverty reduction through job creation, bolstering economic growth (SDGs 1, 8, 10, 12), enhancing agricultural productivity (SDG 2), advancing renewable energy usage and diminishing reliance on fossil fuels (SDG 7), fostering inclusive education and gender equality (SDGs 4, 5, 9), combating climate change (SDG 13), transforming cities into sustainable and harmonious environments (SDGs 11, 16, 17), and curbing environmental pollution (SDGs 3, 6, 12, 14, 15). Nonetheless, the study highlights the need for further efforts to achieve the SDG targets, particularly in part of liquid and solid fertilisers as the AD outputs.

This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical features such as waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is validated through a real-world case study conducted in the UK. Comparative analysis with benchmark models demonstrates a significant improvement in prediction accuracy, increasing from the range of 50-80% with benchmark models to 91% with the proposed framework. The results also show the efficacy of the weekly operation pattern, which leads to a substantial 78% increase in biogas generation during the testing period. Moreover, the pattern contributes to a reduction of 71% in total days required for feeding and 30% in total days required for pre-feeding.