Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning–based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg’s method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.