This overview offers a thorough method for operating a robotic hand with Shimmer3 EMG sensors that emphasizes real-time implementation and gesture recognition and it involves strategically placing five electrodes on the user's forearm to capture EMG signals associated with hand movements, which undergo preprocessing, including filtration and normalization, to enhance signal quality. Feature extraction techniques are then applied to identify unique characteristics of each hand gesture, forming a basis for robust gesture recognition and Through machine learning algorithms, specific EMG signal patterns are correlated with predefined hand gestures, enabling real-time control of the robotic hand. Natural and intuitive human-machine interaction is made possible by the integration of EMG sensors, signal processing, and machine learning and so this has potential uses like in assistive technology, prosthetics, and rehabilitation. The results of the would also demonstrate the viability and efficacy of using Shimmer EMG sensors for accurate and responsive control of robotic prosthetics or assistive devices which would as a result highlight the possibility for improved prosthetic technologies and rehabilitation.