In sports, 3D posture estimation is an essential problem for applications ranging from virtual reality and game creation to performance monitoring and injury prevention. Athletes’ intricate and dynamic motions are difficult to accurately capture because of things like quick motion, occlusions, different camera angles, and a variety of body forms. This work offers a thorough review of current developments in deep learning-based techniques for 3D human pose estimation, with a focus on sports. We explore various deep learning architectures employed for this task, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Convolutional Networks (GCNs), discussing their strengths and weaknesses in handling the unique challenges of sports data. Based on input modalities (monocular image/video, multi-view video, inertial sensors) and output representations (skeletal joints, dense 3D model), we classify and evaluate current methods. The study explores the intricacies of creating and annotating datasets for motion capture in sports, emphasizing the value of extensive, varied, and precisely labeled data. Additionally, we look at popular assessment metrics and talk about how they relate to sports applications. These metrics include Mean per Joint Position Error (MPJPE), Percentage of Correct Parts (PCP), and 3D reconstruction error. We also discuss the drawbacks of existing deep learning techniques, such as their inability to handle severe occlusions, generalize to other sports and athletes, and achieve real-time performance. We conclude by outlining exciting avenues for future study, including improving the integration of temporal information, utilizing multi-sensor fusion, and creating more reliable and effective deep learning models for 3D posture estimation in sports.