Structure-based drug design (SBDD) is a transformative methodology in drug discovery that utilizes the three-dimensional (3D) structure of biological targets, primarily proteins, to develop new therapeutic agents. The process involves identifying a target protein, determining its structure using experimental methods like X-ray crystallography or NMR spectroscopy, and designing molecules to modulate its activity. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL), have significantly enhanced SBDD by automating and refining key steps such as virtual screening, hit optimization, and binding affinity prediction. Techniques such as convolutional neural networks (CNNs), which have revolutionized computer vision, are now being applied to analyze protein–ligand complexes with remarkable precision, accelerating tasks like pose prediction and virtual screening. By integrating AI into SBDD workflows, researchers can achieve higher success rates in identifying lead compounds, minimize toxicity risks, and streamline the drug discovery pipeline. This synergy between SBDD and AI has already contributed to the discovery of clinically approved drugs and promises to redefine the development of new, targeted, and efficient medicines.