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Among traditional approaches, structure\u2010based drug design (SBDD) remains a fundamental approach for drug discovery using molecular docking, scoring functions, and molecular dynamics simulations. However, these approaches are often constrained by limited flexibility, resolution, and generalizability. Geometric deep learning (GDL) offers a transformative alternative by enabling models to learn directly from non\u2010Euclidean molecular representations, such as graphs, point clouds, and meshes, capturing critical 3D spatial relationships inherent to protein\u2013ligand interactions. This review highlights the theoretical underpinnings and practical applications of GDL in small\u2010molecule drug discovery, focusing on tasks including binding affinity prediction, virtual screening, de novo molecule generation, pose prediction, ADMET profiling, and protein flexibility modeling. We explore key GDL architectures, graph neural networks, SE(3)\u2010equivariant networks, 3D convolutional neural networks, point cloud models, and geometric transformers, and assess their performance across various drug discovery benchmarks. The integration of geometry\u2010aware AI models with experimental and computational workflows was also highlighted for its potential to streamline hit\u2010to\u2010lead optimization and advance rational drug design. Despite remarkable progress, the field faces challenges including limited high\u2010quality 3D structural datasets, protein flexibility representation, and the interpretability of deep models. Addressing these issues through hybrid modeling approaches, multi\u2010resolution learning, and self\u2010supervised training could further elevate GDL's impact. 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