Abstract <p>Natural coumarins, a group of bioactive phytochemicals, exhibit a wide range of biological activities, including anticancer, anti-inflammatory, antimicrobial, and antioxidant effects. Owing to their structural diversity and pharmacological properties, coumarins have long attracted scientific interest. This review discusses the emerging role of artificial intelligence (AI) in advancing coumarin-related research and drug discovery. AI-based approaches, such as machine learning and deep learning, are increasingly used to analyze large datasets, predict biological activity, and identify coumarin derivatives with promising therapeutic potential. Furthermore, computational tools like virtual screening and molecular docking facilitate the modeling of coumarin interactions with biological targets, providing valuable insights into their mechanisms of action and potential applications in personalized medicine. AI also significantly accelerates drug discovery by improving structure–activity relationship (SAR) analyses and optimizing lead compounds for preclinical and clinical evaluation. The versatility of AI enables researchers to integrate heterogeneous data sources, uncover novel applications of coumarins, and design more effective and targeted therapeutic agents. Despite existing challenges—including data quality, the need for experimental validation, and computational complexity—the integration of AI into coumarin research presents transformative opportunities for future medical innovations. This review summarizes recent advances, representative case studies, and prospective directions, emphasizing the importance of interdisciplinary collaboration to address current limitations. By combining the inherent therapeutic potential of natural phytochemicals with the computational power of AI, this field is advancing innovative strategies in drug development and expanding the frontiers of modern pharmacology.</p>