Integrating machine learning (ML) into drug discovery represents a significant advancement, transforming traditional approaches and accelerating the development of new therapeutics. This review has highlighted the diverse ML methodologies currently employed in the field, including supervised learning, neural networks, and reinforcement learning, and their applications across various stages of drug discovery. By leveraging ML, researchers can enhance predictive accuracy, streamline drug design processes, and optimize clinical trials, ultimately leading to more efficient and effective drug development.