Hand gesture recognition (HGR) has gained significant traction in human–computer interaction (HCI) due to its intuitive and natural interaction paradigm. This paper explores the potential of MediaPipe for developing real-time HGR systems. MediaPipe offers pre-trained models for hand detection and landmark tracking, which are crucial for extracting hand pose information. We examine how these models can be leveraged for HGR, focusing on (mention your specific research area, e.g., sign language recognition, touch less control, VR interaction). The findings contribute to the advancement of HGR by showcasing the benefits of MediaPipe, including (mention relevant advantages, e.g., real-time processing, accessibility, customization potential). This research paves the way for further exploration of MediaPipe in various HCI applications, particularly in the domain of (mention your specific application area).