Artificial Intelligence Applications in Bioenergy and Biomaterials Development via Fungal Biotechnology: Challenges and Perspectives

The convergence of artificial intelligence (AI) and fungal biotechnology, referred to as biotechnological artificial intelligence (Bio-AI), is rapidly emerging as a powerful approach for advancing sustainable innovations in bioenergy and biomaterials. AI techniques such as machine learning, deep learning, and multivariate analysis are being integrated into fungal biotechnology pipelines to enhance strain selection, guide metabolic engineering, monitor fermentation processes, and predict bioactive compound production. AI is particularly effective in handling complex, high-dimensional datasets generated by omics technologies (genomics, proteomics, and metabolomics), accelerating the discovery of fungal secondary metabolites and high-value bio-based materials. In the bioenergy sector, AI applications are expanding to include feedstock characterization, prediction of conversion efficiency, and supply chain optimization. Despite these advancements, significant challenges remain, including data heterogeneity, limited standardized datasets, issues of model interpretability, and the need for stronger integration between computational models and wet-lab experimentation. Ethical considerations and interdisciplinary collaboration are also essential to ensure responsible and effective deployment. This chapter explores the transformative potential of AI at the intersection of fungal biotechnology, bioenergy, and biomaterials in driving the transition toward a sustainable bio-based economy.