ARTIFICIAL INTELLIGENCE IN HERBAL NANO FORMULATION: A COMPREHENSIVE REVIEW

Abstract

A revolutionary approach to contemporary drug development is the combination of artificial intelligence (AI) and nano-herbal formulation. Conventional herbal remedies frequently have drawbacks such poor bioavailability, inconsistent composition, and a lack of pharmacokinetic knowledge. While AI offers strong tools for data analysis, prediction, and optimisation, nanotechnology enables better phytochemical solubility, stability, and targeted distribution. AI-based deep learning and machine learning models have been used to improve quality control, predict bioactivity, develop appropriate nanocarriers, optimise extraction settings, and evaluate toxicity. These methods promote individualised therapeutic tactics, expedite formulation development, and lessen the workload associated with experiments. While AI- driven optimisation is used, case studies using curcumin, ginseng, and other nano-herbal systems show increased bioavailability and therapeutic efficacy. Notwithstanding its benefits, issues with data accessibility, standardisation, governmental permission, and ethical considerations still exist. All things considered, the combination of AI and nano-herbal technology presents a promising avenue for future pharmaceutical research by offering substantial promise for safer, more effective, and patient-specific treatments.