The past decade has witnessed a transformative “cryoEM revolution” characterized by exponential growth in high-resolution structural data, driven by advances in cryogenic electron microscopy (cryoEM), and cryogenic electron tomography (cryoET). The integration of deep learning technologies into structural proteomics workflows has emerged as a pivotal force in addressing longstanding challenges, including low signal-to-noise ratios, preferred orientation artifacts, and missing-wedge problems that have historically limited efficiency and scalability. This review article examines the application of Artificial Intelligence (AI) across the entire cryoEM pipeline, from automated particle picking using convolutional neural networks (Topaz, crYOLO, CryoSegNet), to computational solutions for preferred orientation bias (spIsoNet, cryoPROS), and advanced denoising algorithms (Topaz-Denoise). In cryoET, tools such as IsoNet employ U-Net architectures for simultaneous missing-wedge correction and noise reduction, while TomoNet streamlines subtomogram averaging through AI-driven particle detection. The workflow culminates with automated atomic model building using sophisticated tools like ModelAngelo, DeepTracer, and CryoREAD that translate density maps into interpretable biological structures. These AI-enhanced approaches have demonstrated remarkable achievements, including near-atomic resolution reconstructions with minimal manual intervention, resolution of previously intractable datasets suffering from severe orientation bias, and successful application to diverse biological systems from HIV virus like particles to in situ ribosomal complexes. As deep learning continues to evolve, particularly with the emergence of large language models and vision transformers, the future promises even more sophisticated automation and accessibility in structural biology, potentially revolutionizing our understanding of macromolecular architecture and function