The COVID-19 epidemic has significantly changed how we work out, with more people turning to home fitness as a way to stay active during stay-at-home orders. However, without access to professional trainers, beginners may struggle to perform exercises with proper form, increasing the risk of injury. Therefore, there is a need for systems to monitor exercise performance for both short- and long-term injury prevention. In this study, we present an approach for accurately detecting and correcting yoga postures using pose estimation techniques with OpenCV and VGG-19 architectures with GPU transfer learning. To precisely measure and correct body posture during training sessions, the suggested solution combines deep learning-based algorithms and computer vision approaches. To confirm the effectiveness of the VGG-19 model on the utilised dataset, We conducted a large number of tests, comparing the performance of several machine learning and deep learning strategies for estimating yoga postures. With a precision of 98.11 percent, the findings show the usefulness of the suggested technique in precisely recognising and correcting exercise postures. The findings of this study have significant implications for improving the effectiveness and safety of yoga sessions and could be extended to other domains that require precise human pose estimation.