Advances and challenges in the integration of artificial intelligence in microbial biosurfactant bioprocess

Microbial biosurfactants are versatile biomolecules with potential to support sustainable chemistry and bio-based solutions for climate action. However, their use remains limited primarily owing to the complexities in their bioprocess. This review aims to evaluate current trends in biosurfactant research, with a special focus on artificial intelligence (AI)-based optimization techniques to clearly bring forth the qualitative research directions, the quantitative tools adopted, and most promising AI driven strategies for enhanced biosurfactant bioprocess. Systematic review revealed Artificial neural network, coupled to Response surface methodology, as the most extensively explored techniques till date, consistently delivering near accurate predictions in process optimization for biosurfactant production. Qualitative analysis suggested the potential of hybrid AI techniques with optimization algorithms to be most promising as they leverage the strength of both approaches. For the first time, this study integrates bibliometric analysis with AI-based review to provide a dual perspective on both research progression and technological innovations, with clear future research directions. Despite notable advancements, challenges such as limited datasets, model transferability, and microbial metabolic diversity persist. The study suggests that open-access, multi-dimensional datasets and hybrid AI models could significantly advance biosurfactant research and industrial scalability.