Abstract Advances in deep learning and generative modeling have transformed the landscape of protein and RNA design, enabling rapid and precise creation of novel biomolecules with tailored structures and functions. In protein design, generative deep learning frameworks now support backbone generation, sequence optimization, and joint sequence–structure co‐design with unprecedented accuracy. These approaches have facilitated broad applications ranging from cyclic peptide and non‐natural fold engineering to functional tool development, including small‐molecule sensing, catalytic center scaffolding, allosteric switching, intracellular logic circuits, and the targeting of intrinsically disordered proteins. Emerging therapeutic applications—such as immune cell engineering, G protein‐coupled receptor‐targeted miniproteins, receptor‐degrading binders, and thermostable antitoxins—demonstrate the translational potential of computational design. Parallel progress in RNA design, driven by enhanced 3D structure prediction models and generative algorithms, is expanding capabilities in aptamer engineering and RNA–protein complexes, despite ongoing challenges in model generalization and experimental validation. Together, these developments highlight a new era of AI‐driven molecular engineering, in which unified protein–RNA modeling, large‐scale sampling, and automated experimental pipelines will accelerate the creation of programmable biological systems and next‐generation therapeutics.