Hand gestures are crucial for developing prosthetic and rehabilitation devices, enabling intuitive human–computer interaction (HCI) and improving accessibility for individuals with impairments. Recently, gesture recognition systems based on surface electromyography (sEMG) have been widely employed in various fields, demonstrating remarkable advantages and developments. In this paper, we present a comprehensive survey on sEMG-based hand gesture recognition. We provide an overview of the basic knowledge and background of sEMG signals and the acquisition equipment used. We delve into the applied feature extraction methods and classification models, focusing on recent advances in deep learning techniques. We also identify the datasets of sEMG signals used for hand gesture recognition. Moreover, we highlight recent applications of sEMG-based gesture recognition methods, including HCI, sign language recognition, rehabilitation, prosthesis control, and exoskeletons for augmentation. Additionally, we outline the latest innovative progress in this field, such as the influence of force, user identity detection, and migration effects. We also discuss the current limitations and challenges. Finally, we summarize the main findings and discuss future directions to enhance sEMG-based hand gesture recognition.