The recognition of hand gestures is increasingly gaining prominence as a means to augment human–computer interactions. Its relevance spans multiple domains, such as sign language interpretation, virtual reality applications, rehabilitation processes, and the development of prosthetic devices. Due to the substantial volume of data in Electromyography signals (EMG), it is essential to develop advanced methods for extracting this information for use in hand gesture recognition (HGR) systems. The development of prosthetic control is rendered more intuitive through the interpretation of user intent derived from electromyographic signals, thereby augmenting human–computer interaction via adaptive interfaces, and facilitating tailored rehabilitation programs for individuals experiencing motor impairments. This manuscript provides a comprehensive overview of the numerous methodologies employed in the classification of Hand Gesture based on EMG signals. The manuscript encompasses a variety of techniques utilized for the acquisition of EMG signals, as well as their preprocessing. Multiple researchers investigate the feature extraction methodologies and classification frameworks, with an emphasis on the recent developments in deep learning approaches. In addition, the datasets of sEMG signals leveraged were specified for the purpose of hand gesture recognition. The efficacy of EMG-based HGR has been assessed in terms of accuracy. This paper aims to assist researchers in selecting the appropriate methodologies for myoelectric HGR.