An extensive overview of the surface electromyography (sEMG) methods for signal processing and their application to prosthetic hand control is given in this paper’s abstract. Techniques for analyzing muscle activity to enable natural and accurate movement of the hands in prosthetic have advanced immensely as a result of growing interest in sEMG-based devices. This article discusses various techniques, such as wavelet transformation, machine learning-based algorithms, and time- and frequency-domain approaches, for feature extraction and classification from sEMG data. It also looks at how deep learning models have recently been included, and how it has helped to increase the precision and stability of sEMG signal classification. In addition, hybrid models that combine traditional statistical techniques with neural networks are investigated for their potential to improve prosthetic control precision and adaptability. The study tackles typical real-time signal recognition problems, like noise reduction and multi-degree freedom movement control management. The review’s conclusion highlights the need for more study on multimodal systems that use machine learning and sophisticated signal processing in order to enhance the usability and reliability of prosthetic devices.