This chapter gives an in-depth coverage of how Quantitative Structure–Activity Relationship (QSAR) modeling has evolved since its inception in the early 1960s to its present embodiment shaped by artificial intelligence (AI) and machine learning (ML). We highlight the historical developments that laid down the fundamentals of QSAR analysis. Many of the early fundamentals are very much valid in the realms of modern machine learning and have been modified to handle much larger and more complicated data. Furthermore, we highlight the limitations and challenges inherent in traditional QSAR methodologies, which were the primary motivation for developing new algorithms for machine learning. We intend that this chapter will serve as a guide, portraying a glimpse of the past, present, and future of this evolving field and providing valuable insights for those aiming to learn the applications of QSAR in chemistry and biology.