Advances in Functional Genomics and Biotechnology for Enhancing Therapeutic Potential of Medicinal Plants

Medicinal plants have long served as a primary source of bioactive compounds with essential therapeutic applications. Recent advances in functional genomics and plant biotechnology now enable precise manipulation of metabolic pathways to enhance the production of specialized metabolites with medicinal value. However, an integrative understanding of how genomic discovery can be linked with pathway engineering, scalable production systems, and healthcare applications remains insufficiently developed. This knowledge gap limits the effective translation of molecular insights into the sustainable production of medicinally important compounds. The novelty of this review lies in its integrated framework linking functional genomic discovery with pathway engineering, synthetic biology, artificial intelligence-assisted prediction, and scalable production systems for medicinal plant-derived therapeutics. This review aims to provide a comprehensive overview of cutting-edge approaches in medicinal plant research, emphasizing high-throughput RNA sequencing, CRISPR/Cas9 gene editing, synthetic biology, and metabolic engineering for optimizing the production of key bioactive compounds, including artemisinin, cannabinoids, ginsenosides, and taxol. It further examines how these tools collectively support metabolite discovery, pathway elucidation, yield improvement, and biotechnological production in major medicinal plant systems. We explore the application of genomic and biotechnological approaches in plants such as Artemisia annua, Cannabis sativa, Panax ginseng, and Taxus baccata to enhance metabolite yields and promote sustainable production. The review highlights case studies that demonstrate how genetic modification, metabolic engineering, and synthetic pathway design have been successfully employed to increase the synthesis of key medicinal compounds. Moreover, we discuss the integration of artificial intelligence and machine learning to predict gene-metabolite relationships, support personalized phytochemical therapies, and facilitate sustainable, large-scale production. Finally, the review addresses the implications of these innovations for the pharmaceutical industry, healthcare, and agriculture, while also highlighting sustainable and scalable directions for future medicinal plant biotechnology.