Background: Global food systems face compounding pressures from population growth, resource scarcity, and climate change. Precision fermentation—the targeted microbial biosynthesis of specific functional compounds—has emerged as a transformative strategy, and its integration with artificial intelligence (AI) represents a frontier of significant biotechnological and sustainability potential.
Objective: To systematically evaluate AI-driven precision fermentation frameworks, their performance advantages over conventional systems, and the opportunities and challenges associated with their industrial adoption in sustainable food production.
Methods: A comparative literature-based analysis of 22 peer-reviewed studies (2016–2024) was conducted, examining AI and machine learning model architectures, fermentation process outcomes, sustainability metrics, and economic feasibility indicators.
Results: AI-integrated systems demonstrated 15–40% gains in fermentation yield, 25–35% reductions in energy consumption, and up to 50% decreases in production costs compared to conventional fermentation. Deep learning models achieved >92% accuracy in metabolic pathway prediction, while reinforcement learning algorithms optimised real-time bioprocess control with a 30% improvement in resource utilisation efficiency.
Conclusion: AI-driven precision fermentation represents a scientifically validated, industrially feasible pathway toward sustainable, high-efficiency food production. Key challenges—data quality, model interpretability, regulatory harmonisation, and computational scalability—must be addressed to enable broad adoption.