The paper aims to assist individuals in performing physical exercises with precision and safety. This model delivers real-time feedback on an individual’s workout style by utilising powerful computer vision and machine learning technology. It uses deep learning models for position estimation to recognise and track essential body features while exercising. It classifies workouts and their phases, such as “down” and “up,” based on a continual monitoring of the user’s body position. Advanced computer vision algorithms are used to analyse video frames from the camera feed in order to extract critical data about the user’s form. It can recognise and classify certain workouts such as the deadlift, squat, and push-up by continually monitoring the user’s body position. The system provides useful information such as the current stage of the exercise, the number of repetitions, and the chance of correct performance. The model can recognize and detect the exercises performed by the person and it also iterates the number of times the exercises are done and it also detects the position of the body of the person. Healthcare professionals who want to promote safe and successful exercise regimens.