The worth of human beings remains enormous and deep regardless of physical limitations, including those who are not physically handicapped. In the context of technology for people with disabilities, EMG signals play an essential role in developing and operating various devices such as prosthetic limbs and Human–Computer Interaction (HCI) employing machine learning and deep learning algorithms. The EMG pattern recognition system (EMG-PR) is made up of multiple interconnected components, including signal acquisition, noise reduction, feature extraction, and pattern classification. In this paper, pattern recognition is performed on a dataset of hand movements which are accessible in Rami. N. Kushabha’s repository. AFA value focuses on the temporal features of the movement. The retrieved AFA value, together with RMS and waveform length is used for classification by machine learning classifiers. Cross-validation is performed on the train-test split dataset for each classifier. Based on the highlighted dataset, assessment measures such as recall, accuracy, and F1 scores are calculated. Experimental findings show that the recommended feature extraction approach outperforms classification using other conventional features. The recommended classification strategy of Random forest yields 92.62% whereas decision trees, naive Bayes, k-nearest neighbors, and Adaboost, convolutional neural networks with accuracy values of 92.26%, 84.44%, 91.95%, 90.73%, and 98.91% respectively.