Quantitative Structure–Activity Relationship (QSAR) is an important computational technique used in modern drug discovery to predict the biological activity of chemical compounds based on their molecular structures. QSAR models establish mathematical relationships between chemical structure descriptors and biological activities. This method significantly reduces the cost, time, and resources required for experimental screening of large numbers of compounds. QSAR plays a major role in medicinal chemistry, toxicology, and environmental chemistry by helping researchers understand how structural features influence biological activity. The development of QSAR involves dataset preparation, calculation of molecular descriptors, model development, validation, and prediction. Modern QSAR approaches also integrate machine learning and artificial intelligence to improve prediction accuracy. This review article provides a clear and systematic overview of QSAR concepts, types of QSAR models, molecular descriptors, model development processes, validation techniques, applications, advantages, and limitations. The article is intended to provide fundamental knowledge of QSAR for learners and moderate-level researchers in the fields of medicinal chemistry and computational drug design.