Athletes and casual gym-goers often risk injury when performing squats due to poor form. To address this problem, this project proposes an artificial intelligence-based system that uses computer vision and machine learning to monitor squat posture and provide real-time corrective feedback. The system leverages Mediapipe for pose estimation and K-Nearest Neighbors (KNN) for classification of squat form [8]. Major challenges included maintaining model accuracy, processing video data on a Raspberry Pi, and adjusting for different squat variations. Through experimental testing, the AI demonstrated 90–94% accuracy in identifying proper and improper squats, even when adapting to elevated squat styles. Compared to previous methodologies, this system improves by providing immediate feedback rather than post-set evaluations. Ultimately, this project presents a lightweight, affordable, and portable solution to improve exercise safety and performance, reducing the risk of serious injuries in athletic and fitness communities.