Volume-2 ,Issue-4, April-2026

The therapeutic application of medicinal plants, or phytomedicine, has a long history and is still vital to the world's healthcare system. The field confronts obstacles despite its potential, such as uneven quality, poor clinical validation, safety issues, and variability in the content of bioactive compounds. These problems can be revolutionized by recent developments in artificial intelligence (AI), which offer tools for quick bioactive compound discovery, precise plant identification and authentication, pharmacological activity prediction modeling, and formulation optimization for individualized treatment. AI technologies, such as computer vision, machine learning, deep learning, and natural language processing, make it possible to combine traditional knowledge with contemporary pharmacological research, improving the safety, efficacy, and repeatability of herbal remedies. The main uses of AI in phytomedicine are highlighted in this paper, including quality control, toxicity and dosage prediction, integration with databases used in traditional medicine, and the identification of new herbal chemicals. Prominent case studies show how AI-driven methods might improve precision herbal therapy. Additionally, the paper highlights the significance of AI in transforming phytomedicine into a data-driven, scientifically verified, and customized therapeutic platform while discussing present limitations, ethical issues, and future potential.

Keyword: Phytomedicine, Artificial Intelligence, Machine Learning, Deep Learning, Traditional Medicine, Herbal Drug Discovery, Quality Control, Precision Herbal Therapy