This is an innovative computer vision project that integrates MediaPipe, OpenCV, and PyAutoGUI to enable hand gesture-based control of the computer cursor in real-time. This project captures video input from a webcam and processes each frame to detect hand landmarks, allowing users to control the mouse cursor with their hand movements and perform actions such as clicking and double-clicking using specific gestures. The project starts by initializing the MediaPipe Hands module for hand landmark detection and the PyAutoGUI library for controlling the mouse cursor. It captures video from the webcam and processes each frame to identify key points on the hand, such as the tips and metacarpophalangeal (MCP) joints of the fingers. The coordinates of these landmarks are mapped to the screen dimensions, enabling the translation of hand movements into cursor movements. Key features include real-time hand detection and tracking, ensuring immediate and responsive cursor control, and using the index finger tip's position to control the cursor movement across the screen for intuitive and precise navigation. The project includes gesture recognition to perform mouse click actions, where if the middle finger tip is below its MCP joint, a click action is triggered, and if the pinky finger tip is below its MCP joint, a double-click action is performed. Detected hand landmarks are drawn on the video feed using OpenCV, providing visual feedback to the user and enhancing the interaction experience