AI-driven optimization of bioactive compounds extraction from food byproducts: research progress and application prospect

Food byproducts are rich in bioactive compounds with nutritional and preservation benefits, yet conventional extraction methods are limited by high solvent use, time-consuming, and poor scalability. Green technologies such as ultrasound-assisted extraction (UAE), supercritical fluid extraction (SFE), and microwave-assisted extraction (MAE) offer sustainable alternatives but require precise optimization of complex, non-linear parameters. This review highlights artificial intelligence (AI) as a transformative tool to overcome these challenges by enabling predictive modeling, real-time optimization, and intelligent process control of extraction processes. Unlike previous reviews focusing mainly on green extraction techniques, this work uniquely synthesizes recent progress on AI-driven approaches. It critically compares their performance against traditional methods such as response Surface methodology. Case studies include how AI models, including artificial neural networks, support vector regression, and hybrid algorithms, deliver higher yields, lower energy use, and improved reproducibility. The review further addresses industrial applications, regulatory gaps, and commercialization challenges, offering future research directions for scalable and interpretable AI frameworks. Combining sustainability, efficiency, and innovation, this review positions AI-driven extraction as a frontier for advancing functional food development and circular bioeconomy strategies.