The field of drug design and understanding the interactions of drugs with biological systems in medicinal chemistry and pharmacology is crucial. Quantitative structure-activity relationship (QSAR) serves as a powerful tool in this domain, facilitating the establishment of quantitative and predictive relationships between the chemical structure of a molecule and its biological or pharmacological activities. This chapter conducts a comprehensive exploration of QSAR, covering its fundamental principles, evolutionary trajectory in computational chemistry, and current and future applications in the dynamic landscape of drug discovery. A diverse range of methods, encompassing statistical techniques as well as advanced machine learning and deep learning approaches, is presented as influential tools for precise drug design. The chapter emphasizes the significance of model development and validation, ensuring the reliability and accuracy of QSAR predictions. Moreover, it highlights the emerging trends and advanced techniques like deep learning, graph neural networks, multimodal and multi-task learning, transfer learning, multi-target QSAR, chemogenomics, and proteochemometrics that offer innovative possibilities for drug design. QSAR continues to be an important approach in the pharmaceutical and chemical industries, providing predictive capabilities and data-driven insights.