Surface electromyography (EMG) serves as a pivotal tool in hand gesture recognition and human-computer interaction, offering a noninvasive means of signal acquisition. This study presents a novel methodology for classifying hand gestures using EMG signals. To address the challenges associated with feature extraction where, we explored 23 distinct morphological, time domain and frequency domain feature extraction techniques. However, the substantial size of the features may increase the computational complexity issues that can hinder machine learning algorithm performance. To mitigate this, we employ an efficient feature selection approach, specifically an extra tree classifier. The selected potential feature fed into the various machine learning-based classification algorithms where our model achieved 97.43% accuracy with the KNN algorithm and selected feature. By leveraging a comprehensive feature extraction and selection strategy, our methodology enhances the accuracy and usability of EMG-based hand gesture recognition systems. The higher performance accuracy proves the effectiveness of the proposed model over the existing system.