<p>To decode the motion intention from the surface electromyography signals (sEMG) for designing multi-functional prosthetic devices, machine learning (ML) and deep learning (DL) techniques have been widely adopted. Nevertheless, the complexity of the prosthetic hand and the non-stationary characteristics of sEMG signals introduce several practical challenges in adopting ML/DL techniques in the myoelectric prosthetic design. To this end, considerable research attention has been paid to enhance the model reliability, adaptation and robustness. In this article, we present a comprehensive review on the latest advancements in sensing modalities in prosthetics, publicly available datasets, prominent features for classifier training and the feature selection techniques used in the classifier model. Specifically, this article presents a survey on ML and DL techniques for myoelectric hand prosthetic control spanning nearly two decades (2007–2024) by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and using Web of Science and PubMed databases. This scoping review also elucidates the potentials of different sensing modalities in myoelectric control other than sEMG signal for enhancing the robustness and reliability of the prosthetic device. Moreover, open research challenges and emerging research directions in terms of hardware design, multi-modal sensing, and motion decoding techniques are also critically analyzed to provide insights on future developments.</p>