This paper examines the transformative effects of artificial intelligence (AI) on predicting protein structures, a pivotal aspect of structural biology. Traditional techniques such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy have been the gold standards for determining protein structures, but these methods are often expensive, labor-intensive, and time-consuming. The introduction of AI, especially through DeepMind's AlphaFold, has dramatically changed this scenario by achieving prediction accuracies comparable to experimental methods. This review covers the methodologies behind AI-driven predictions, including machine learning and deep learning techniques, and highlights recent advancements and applications. Notable successes, such as AlphaFold's accurate prediction of the SARS-CoV-2 spike protein structure, illustrate the practical benefits of AI in speeding up drug discovery and biomedical research. Despite these advances, challenges remain, such as modeling membrane proteins, handling large datasets, and enhancing model interpretability. Future directions include the integration of dynamic data, the use of generative adversarial networks (GANs), and combining datadriven and physics-based approaches to further improve prediction accuracy and applicability. The paper underscores the importance of interdisciplinary collaboration in leveraging AI's full potential in structural biology and discusses the broader impacts on biotechnology and personalized medicine. Ongoing research and innovation in this area are poised to revolutionize our understanding of biological systems and propel therapeutic development forward.