The global food system faces unprecedented challenges, including climate-driven agricultural instability, rising malnutrition, and consumer demand for sustainable yet appealing products. Artificial intelligence (AI) has emerged as a transformative force in addressing these challenges, enabling breakthroughs from microbial engineering to individualized dietary solutions. This review synthesizes advances in AI-driven precision fermentation—where CRISPR-based microbial optimization and reinforcement learning accelerate bioactive compound synthesis—and hyper-personalized nutrition, where predictive modeling tailors diets to genetic, metabolic, and cultural profiles. We highlight how AI decodes sensory attributes (e.g., flavor and texture) through deep learning and natural language processing, bridging gaps between lab-scale innovation and consumer acceptance. However, the adoption of these technologies raises critical ethical concerns, including data privacy risks from wearable health monitors and algorithmic biases exacerbating nutritional disparities. Key findings include 300% yield increases for alt-proteins via AI-CRISPR fusion, 60% reduction in bioreactor failures through RL optimization, and 25% lower childhood anemia rates via equitable AI-nutrition platforms—though ethical gaps persist in data privacy (72% GDPR non-compliance) and algorithmic bias. By analyzing regulatory frameworks and proposing equity-focused design principles, this article advocates for a balanced approach to AI deployment in food systems. Emphasis on digital transformation, we underscore AI’s potential to democratize sustainable food production while urging collaborative governance to ensure transparency and inclusivity. This work serves as a roadmap for researchers, policymakers, and industry stakeholders navigating the intersection of AI, biotechnology, and nutrition science.
Priyadharshini et al. (Thu,) studied this question.