In recent years, there has been interest in the intersection between pharmaceutical research and the field of artificial intelligence. AI aims to develop computer systems capable of emulating tasks associated with human intelligence, such as problem-solving and decision-making . As a subset of AI, machine learning generally refers to computational models that can extract information and learn from data. Fundamentally, ML algorithms find patterns in data to make predictions, related to, but distinct from statistical methods . Briefly, the two main types of ML techniques are supervised and unsupervised learning. With the former, output values are known for the corresponding input data on which the model is trained. In this way, supervised learning is used in classification or regression tasks, whereby some input is mapped to a discrete output label or continuous output value, respectively . In contrast, unsupervised learning is predicated on uncovering patterns based on unlabeled data. This is commonly utilized for exploratory analysis and clustering, to identify inherent structures and meaningful groups . While the intricacies of ML algorithms are discussed in detail elsewhere , the potential of these models in pharmaceutical formulation development is briefly reviewed.