Abstract
Medicinal plant research has long been important to human health, but conventional approaches are frequently time-consuming and labor-intensive. The identifi cation, categorization, and medicinal uses of plant-based chemicals are being transformed by the introduction of artificial intelligence (AI) into this field. The main AI-driven approaches in medicinal plant research are examined in this article, such as phytochemical profiling, plant identification and categorization, and predictive modeling for bio activity and disease therapy. In addition to highlighting AI applications like machine learning (ML), deep learning (DL), and natural language processing (NLP) in plant taxonomy, drug development, virtual screening, and customized medicine, this study synthesizes data from top scientific databases. Researchers can boost biodiversity evaluation, increase agricultural output, and advance conservation efforts by integrating AI with traditional ethnobotanical expertise. Additionally, AI-powered tools help to improve disease prognosis and treatment strategies, opening up more effective avenues for medication development. The approaches, advantages, difficulties, and prospects for applying AI to the analysis of medicinal plant species, the identification of bioactive compounds, and the development of new treatments are all covered in this article. While overcoming the drawbacks of conventional plant research techniques, AI’s revolutionary role in improving the speed and precision of medicinal plant research has exciting prospects for improving healthcare and ecological conservation.