Gesture control systems are becoming vital across industries due to their ability to strengthen user interaction, accessibility, and provide innovative ways of interfacing with technology. As these systems continue to grow, they will likely become an indispensable component of user interfaces across a wide range of applications. In this work, a Gesture Control System is designed and implemented to enhance the human-drone interaction paradigm. The proposed system leverages machine learning techniques to interpret and respond to user-defined gestures, providing a seamless and natural interface for controlling drones. The work begins with an exploration of existing gesture recognition methodologies and their limitations, leading to the development of a robust model tailored for drone control. This is implemented as a real-time image processing pipeline that captures and interprets gestures using a combination of depth sensing and machine learning, enabling users to convey complex commands effortlessly. The methodology involves integrating a ground station system with the drone controller, which captures live video data to be processed by the gesture recognition algorithm in real-time. The machine learning model, trained on a diverse dataset of gestures, accurately classifies user-defined commands, allowing for precise and responsive drone control. The system’s performance is assessed through a series of experiments, evaluating its accuracy, latency, and reliability in various environmental conditions.