In this research paper presents the applicability of machine learning techniques for classifying surface Electromyogram (sEMG) signals. sEMG signals captured from a person’s muscles provide positive insights into the field of biomechanics of human movement. Classifying sEMG signals precisely is one of the challenging problems and can greatly benefit applications like the control of prosthetic hands. However, current research in sEMG-based gesture classification faces several challenges such as inaccurate classifications. To overcome these issues, this paper compares the different learning techniques with grid search hyperparameter tuning used to classify the EMG data sets. The datasets comprise five healthy subjects (two males and three females) for different muscle activities. The presented model gives an accuracy of 84% for classification for various experimentation and validation, outperforming ten existing classification models. These results indicate these classification models can be used in healthcare applications, including movement intention detection and controlling advanced prostheses.