This study proposes a human posture action detection system based on image processing and machine learning, designed to automate fitness exercise counting and eliminate errors associated with manual tracking. The framework begins by capturing and decomposing test videos into sequential motion frames using OpenCV. The acquired images undergo preprocessing for enhancement, followed by the detection and normalization of 33 skeletal keypoints via the Mediapipe library. The normalized keypoint coordinates are then transformed into embedding vectors as model input. A K-Nearest Neighbors (KNN) algorithm compares these vectors against a reference database to classify and count exercise repetitions. Temporal smoothing refines the classification output, ensuring robust action recognition and accurate repetition counting. The system culminates in a user-friendly UI that visually presents the processed video stream alongside real-time analytics. By addressing the limitations of conventional counting methods, this research delivers an efficient, integrated solution for automated posture recognition and repetition tracking. Its practical implementation offers significant societal and economic value by enhancing workout efficiency and promoting health-conscious fitness practices.