Protein structure prediction, a cornerstone of structural biology, has been revolutionized by deep learning methods. This exploration explores the fundamental aspects of protein structure, traditional and modern prediction methods, and the revolutionary impact of deep learning. This chapter discusses applications in drug discovery, protein engineering, and understanding disease mechanisms. Deep learning models have made substantial advancements in predicting protein structures, particularly those with complex features and dynamic properties. By leveraging large datasets and powerful algorithms, these models can accurately predict protein structures, enabling a more profound comprehension of biological processes and the creation of innovative therapeutic solutions. This book chapter highlights recent advancements in deep learning-based protein structure prediction, including methods for predicting protein complexes, conformational changes, and evolutionary trajectories. Also, the chapter presents the integration of deep learning with traditional bioinformatics tools and addresses the challenges posed by structural complexities and the need for high-quality datasets. Finally, the authors present future directions, such as modeling dynamic protein conformations and the implications for drug discovery.