In recent times, there have been several advancements in computer vision and image processing, and when combined with machine learning models, is very helpful in posture recognition applications. Posture detection is a useful tool in sports and fitness, as it helps people avoid injuries caused by poor alignment and achieve optimal posture in order to stay healthy. This paper reports on “PoseNet: A Novel YOLO-Driven Framework for Badminton Posture Detection and Correction”, which is a Python-based application that utilizes Roboflow for dataset construction, annotation and augmentation, YOLOv5 for custom training the model on the dataset, and MediaPipe for giving corrective suggestions to the user. The novelty of this framework lies in its dual-stage architecture, combining YOLOv5 for classification and MediaPipe for real-time correction, specifically tailored for badminton. Additionally, it leverages a badminton-specific dataset, ensuring domain relevance and precise analysis. It predicts the stance that the player is planning to achieve and then tells whether the stance is correct based on their key points. The model obtained a high classification accuracy, with mAP50 value of 96.2% and mAP50-95 value of 81.1%.