Yoga pose detection and correction using Deep Learning has become a pivotal application in the fields of computer vision, healthcare, and fitness. The accurate identification and alignment of yoga postures are crucial to prevent injuries and ensure practitioners maximize the benefits of their yoga practice. This paper provides a comprehensive review of Deep Learning techniques employed for yoga pose detection, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid models like CNN-GRU. These models enable pose detection and correction through real-time feedback, ensuring practitioners maintain correct form during their sessions. Key pose estimation frameworks such as PoseNet, BlazePose, and OpenPose are evaluated based on their real-time performance, accuracy, and practical application across diverse environments. The paper also explores challenges such as dataset limitations, handling dynamic transitions between poses, and the computational complexity involved in deploying these systems on mobile or low-resource platforms. Finally, the paper identifies future research directions, including multi-user detection, enhanced temporal modeling for immersive yoga practice experiences. Through this analysis, the paper aims to contribute to the development of more effective, accessible, and safe yoga pose detection systems using deep learning techniques.