In the realm of interactive gaming, we present a groundbreaking Temple Run-style game that leverages hand gestures as a novel mode of control. By harnessing state-of-the-art hand tracking technology, including MediaPipe, the main aim is to deliver an immersive gaming experience where players can intuitively control in-game actions through hand movements. This technology exhibits promise across various domains, including gaming, surveillance, security, and even autonomous vehicle control. Here, the paper’s methodology comprises four key phases: Analysis, Concept Design, Implementation, and Evaluation. During the Analysis phase, here focus on hand gesture recognition using 21 hand landmarks detected by MediaPipe to translate these gestures into meaningful in-game actions. In the Concept Design phase, here envision a racing game where players navigate obstacles using traditional keyboard controls and have the option to use webcam-captured hand gestures for additional control. In the Implementation phase, the technical aspects, Python programming, and integration of libraries like Mediapipe, cv2, numpy, uuid, os, and keyboard to achieve real-time hand tracking and game control. Finally, in the Evaluation phase, we demonstrate how hand gestures effectively replace keyboard inputs for left and right movements within the game. The result is a fully functional hand gesture-controlled car racing game where webcam-detected gestures seamlessly translate into in-game actions, enhancing the gaming experience and showcasing the potential of merging computer vision and gaming technology. Gesture-controlled gaming has the capacity to redefine the gaming landscape, offering players a more immersive and interactive gaming experience. The trained CNN model gives an accuracy of 98%.