Computer vision has become a fundamental technology in various domains, enabling machines to interpret and analyze visual data in real time. This study explores the application of computer vision in interactive gaming by developing a digital air hockey simulation that allows players to control paddles using hand-tracking technology. The system utilizes Python and pre-built computer vision libraries, including OpenCV and MediaPipe, to detect and track hand movements, eliminating the need for physical controllers. The paper provides a detailed overview of object recognition methods, highlighting their importance in real-time image processing. Additionally, deep learning models are discussed in the context of object classification and feature extraction. The implementation of the air hockey game is examined, outlining its modular structure and potential applications beyond entertainment, such as rehabilitation therapy, marketing, and mixed reality experiences. Furthermore, the research introduces an arcade-style Turing Test, where players must distinguish between human and AI-controlled opponents in a hand-tracked air hockey game. This experiment aims to analyze AI-generated movement patterns and their ability to mimic human behavior. This study demonstrates the broader impact of computer vision by leveraging cost-effective technologies and scalable solutions. It emphasizes its accessibility and practicality across multiple industries.