<p>Traditional medicine (TM) has been practiced for millennia and remains vital in healthcare. However, its pharmacological mechanisms are not fully understood by modern scientific standards. Emerging research methodologies now allow for a more systematic exploration of TM’s multi-component and multi-target interactions. This review aims to explore emerging trends in pharmacological research of TM, focusing on advanced methodologies like network pharmacology, metabolomics, artificial intelligence (AI), and molecular docking. It examines how these methods contribute to understanding mechanisms of action, optimizing therapeutic efficacy, and integrating TM into modern medicine. A comprehensive literature review was conducted using major academic databases, including PubMed, Web of Science, and Scopus. Studies relevant to TM pharmacological research, with an emphasis on network pharmacology, metabolomics, AI applications, and molecular docking, were selected. These methodologies have identified multi-target interactions within classical TM formulations, aiding in the optimization of herbal prescriptions. Advances in metabolomics, proteomics, and lipidomics have provided insights into TM-induced biochemical changes. AI-driven techniques like deep learning and big data analytics support virtual screening, herb-drug interaction predictions, and precision medicine in TM. Molecular docking has improved the identification of bioactive compounds and therapeutic targets. Emerging technologies have revolutionized TM pharmacological research, bridging traditional knowledge with modern scientific validation. The integration of computational and experimental methods enhances TM’s applicability in modern healthcare. Further research is necessary to refine these approaches, ensuring wider acceptance of TM in evidence-based medicine.</p>