In modern drug discovery, the quantitative structure–activity relationship (QSAR) has appeared as an efficient computational aid that helps predict biological activity based on the molecular structure of unknown compounds and identify key properties describing their activeness. Secondary metabolites derived from plants possess therapeutic properties because they comprise several classes of bioactive compounds such as alkaloids, flavonoids, terpenoids, phenols, etc., having diverse bioactivities. This chapter provides a review of QSAR models used in chemical series derived from natural products, elucidating their underlying mechanisms, practical applications in the strategic development of pharmaceutical drugs, and their use in exploring natural bioactive compounds. Additionally, a few case studies highlight the successful implementation of the QSAR model in lead identification and guided the optimization of plant-derived molecules and their derivatives for anticancer, anti-inflammatory, anti-immunomodulatory, antitubercular, and antimalarial. These models could be applied to screen potential lead molecules against specified diseases and a framework for modern drug design processes in the future.