The pharmaceutical industry faces increasing complexity in drug formulation development, where traditional trial-and-error approaches are resource-intensive and time-consuming. Machine learning has emerged as a transformative technology enabling data-driven optimization of pharmaceutical formulations through predictive modeling and intelligent design strategies. This review examines the applications of machine learning in pharmaceutical formulation optimization, focusing on solubility prediction, stability assessment, nanocarrier design, and controlled-release system development. Various machine learning algorithms, including artificial neural networks, support vector machines, random forests, and deep learning architectures, have demonstrated significant utility in predicting formulation performance, reducing development timelines, and enhancing product quality. The integration of machine learning with design of experiments and high-throughput screening platforms enables hybrid computational-experimental workflows that accelerate formulation optimization. Despite challenges related to data quality, model interpretability, and regulatory acceptance, machine learning continues to reshape pharmaceutical development paradigms. This article provides a comprehensive overview of current applications, methodologies, limitations, and future directions of machine learning in formulation sciences, highlighting its potential to revolutionize drug delivery systems and personalized pharmaceutical development.