Real-time American Sign Language (ASL) hand gesture recognition system using MediaPipe Hands and a machine learning pipeline based on Random Forest classification. The system detects hand landmarks from webcam input, extracts and normalizes 21 key points, and maps them into feature vectors for predicting alphabet gestures (A–Z). Designed as a lightweight computer vision solution, it integrates data collection, preprocessing, model training, and live inference into a complete pipeline.