Intelligent transformation of ultrasound-assisted novel solvent extraction plant active ingredients: Tools for machine learning and deep learning

Ultrasound-assisted novel solvent extraction enhances plant bioactive compound yield via cavitation, mechanical, and thermal mechanisms. However, the high designability of novel solvents, the multiple influence factors for extracting results, the complexity of extraction mechanisms, and the safety of extraction equipment still pose many challenges for ultrasound-assisted extraction (UAE). This review highlights advancements in utilizing machine learning and deep learning models to provide actionable solutions for UAE challenges, which include accelerating novel solvent screening, promoting the discovery of active ingredients, optimizing complex extraction processes, in-depth analysis of extraction mechanisms, and real-time monitoring of ultrasound equipment. Challenges such as model interpretability, dataset standardization, and industrial scalability are discussed. Future opportunities lie in developing universal predictive frameworks for ultrasound-related technologies and fostering cross-disciplinary integration of AI, computational chemistry, and sustainable engineering. This interdisciplinary approach aligns with the goals of Industry 5.0, fostering a transition toward digitized, eco-efficient, and intelligent extraction systems.