(untitled) (International Journal of Research in Pharmaceutical Sciences and Technology, 2026)
Artificial Intelligence (AI) and Machine Learning (ML) are transforming healthcare research by enabling data-driven discovery, optimization, and quality assurance across nutraceuticals, AYUSH systems, and herbal therapeutics. These technologies facilitate rapid screening of bioactive phytoconstituents, prediction of pharmacological activities, toxicity assessment, formulation optimization, and personalized nutrition strategies. In the context of Indian Systems of Medicine (AYUSH), AI-assisted analysis of classical literature, omics datasets, ethnopharmacological knowledge, and clinical evidence supports scientific validation and accelerates translational research. Machine learning algorithms further enhance herbal drug discovery through virtual screening, biomarker identification, and predictive modelling of herb–drug interactions. AI-based analytical platforms integrated with chromatographic and spectroscopic techniques improve authentication of medicinal plants, detection of adulteration, chemoprofiling, and batch-to-batch consistency. These advances strengthen the standardization of herbal medicines by ensuring identity, purity, potency, safety, and efficacy while complying with global regulatory expectations. Furthermore, AI-driven decision-support systems can improve pharmacovigilance, quality control, supply chain traceability, and sustainable utilization of medicinal plant resources. Despite these opportunities, challenges remain regarding data quality, model interpretability, regulatory acceptance, ethical governance, and integration with traditional knowledge systems. Collaborative efforts among pharmacognosists, AI scientists, clinicians, and regulatory agencies are essential to develop reliable, transparent, and validated AI-enabled frameworks. This presentation highlights the emerging applications of AI and ML in nutraceuticals, AYUSH research, herbal therapeutics, and standardization, emphasizing their potential to bridge traditional wisdom with modern computational science for evidence-based healthcare and global acceptance.