AI-Enabled Green Manufacturing of Hepatoprotective Herbal Medicines: Sustainable Approaches to Phytochemical Extraction and Standardization

Abstract

Liver disorders continue to be a significant worldwide health concern, and hepatoprotective herbal medicines derived from plants such as Silybum marianum, Curcuma longa, Andrographis paniculata, and Phyllanthus species continue to occupy a central role in their management. However, standard extraction and standardization workflows are often solvent-intensive, energy-demanding, and poorly replicable, making tension with modern sustainability and regulatory expectations. This review synthesizes new literature on the convergence of artificial intelligence (AI) and green chemistry in the manufacture of hepatoprotective phytopharmaceuticals. We inspect green extraction platforms: supercritical CO2 extraction, ultrasound- and microwave-assisted extraction, and natural deep eutectic solvents, alongside AI/machine-learning instruments comprising artificial neural networks, response surface methodology hybrids, genetic and particle-swarm algorithms, and chemometric fingerprinting for quality control. Comparative resource-use trends, green chemistry metrics, and representative case studies (silymarin, curcuminoids, customary Chinese medicine quality markers) are presented through tables and schematic figures. The review concludes that AI-guided process optimization can significantly improve extraction productivity, batch consistency, and environmental performance, while highlighting persistent problems in scale-up, data standardization, and regulatory validation that must be addressed for extensive and prevalent industrial adoption.