Others This article explores the application of unsupervised learning techniques in the pharmaceutical industry, focusing on their role in uncovering patterns, discovering insights, and supporting decision-making processes. Unsupervised learning, a subset of machine learning, involves algorithms tasked with identifying patterns and relationships within data without the use of labeled examples. We provide an overview of prevalent unsupervised tasks such as clustering, dimensionality reduction, anomaly detection, association rule mining, and topic modeling, along with popular techniques employed in each task. The article discusses how these techniques can be applied to various pharmaceutical applications, including exploratory analysis, data visualization, anomaly detection, pharmacovigilance, literature mining, and competitive intelligence. We highlight the significance of unsupervised learning in aiding target identification, patient stratification, adverse event detection, understanding compound or disease characteristics, and extracting insights from large text datasets. Additionally, we emphasize the importance of domain expertise and validation in interpreting results from unsupervised learning methods to ensure the reliability and relevance of findings in pharmaceutical research, development, and manufacturing. Through this exploration, we aim to provide insights into the valuable role unsupervised learning plays in advancing pharmaceutical research, optimizing processes, and improving patient outcomes.