Protein structure prediction is a critical challenge in computational biology with significant implications for understanding biological functions, drug design, and disease mechanisms. Traditional methods for protein structure prediction often face limitations in accuracy and efficiency. In recent years, the integration of deep learning techniques with molecular dynamics simulations has emerged as a promising approach to tackle this complex problem. This research paper explores the synergy between deep learning and molecular dynamics simulations for predicting protein structures. We begin by presenting an overview of the fundamental principles of protein structure and the importance of accurate structure prediction in biological research. We highlight the challenges faced by traditional methods, including the combinatorial nature of the protein folding problem and the high computational cost of simulating complex biomolecular systems. Next, we delve into the innovative approach of utilizing deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in conjunction with molecular dynamics simulations. We discuss the advantages of deep learning in capturing complex features from protein sequences and structures. Furthermore, we explore how molecular dynamics simulations provide valuable dynamic information, which can be integrated into the deep learning framework to refine and enhance structure predictions. Throughout the paper, we review recent advancements in this field, highlighting key studies that showcase the successful application of deep learning and molecular dynamics simulations for predicting protein structures. We also discuss the challenges and open questions in this area, including the need for large and diverse training datasets, the development of specialized deep learning architectures, and the incorporation of physical constraints. In conclusion, the combination of deep learning and molecular dynamics simulations holds great promise for advancing the field of protein structure prediction. This research paper contributes to the understanding of this innovative approach and emphasizes its potential to revolutionize our ability to predict protein structures accurately, thereby driving advancements in drug discovery, molecular biology, and personalized medicine.