In everyday interactions between humans and computers, recognizing hand gestures plays a crucial role as it offers a natural and easy way to control and communicate with various applications. This article delves into a detailed exploration of how MobileNet architecture can enhance the accuracy and efficiency of hand gesture detection systems. The aim is to address the limitations found in traditional models by leveraging the optimization and computational efficiency offered by MobileNet. To understand the significance of incorporating MobileNet architecture, the article begins by examining previous research methods used in hand gesture recognition. By conducting a thorough review of existing literature, the study identifies areas where improvements can be made. It then introduces a novel approach that utilizes MobileNet architecture to elevate the precision and effectiveness of hand gesture detection. This approach is tested using a well-established dataset of American Sign Language movements, providing a reliable foundation for training and evaluating the model. This work delves into the technical aspects of implementing the MobileNet-based hand gesture detection system. It explains how the model framework integrates MobileNet architecture and discusses the preprocessing techniques employed for image analysis. The experimental and analytical work conducted during the project is highlighted, emphasizing the iterative process of refining and optimizing the model to achieve optimal performance.