This paper aims to introduce a deep learning-based architecture for hand gesture classification and introduce a potential pathway for the improvement of Brain Computing Interfaces (BCI)-fostered motor rehabilitation systems. The proposed mechanism included inspecting the sequential and ensemble Electromyography (EMG) denoising strategies to remove potential artifacts from the EMG signals, both the strategy included working with Dynamic Mode Decomposition (DMD), Variational Mode Decomposition (VMD), and Tunable Q-factor wavelet transform (TQWT). The refined EMG signals are fed in the custom-designed Convolutional Neural Network (CNN)–Bidirectional Long Short-Term Memory (BiLSTM) based deep neural network architecture suited for the classification task. The research task is performed in three steps, the first step includes training the deep learning model without any artifact rejection, the second step comprises training the model with sequential EMG denoising, and the third step includes training the model with ensemble EMG denoising. The results depicted commendable performance with the ensemble denoising approach reaching an accuracy of 99%, thereby curving a potential pathway for the enhancement of BCI-fostered motor rehabilitation systems.