Individuals with amputations often rely on prosthetic limbs to maintain daily functionality; however, over time, the performance of these devices can be compromised by wear, signal degradation, or other technical issues. In this study, we investigate the enhancement of electromyography (EMG) signals to mitigate changes in signal characteristics associated with long-term use by amputees. Our approach employs deep neural networks (DNN) integrated with various optimization algorithms. Data were acquired using an MYO Armband on the right arms of seven volunteers performing repeated fist clenching until muscle fatigue set in. The acquired data were augmented using synthetic data generation techniques and subsequently processed with a DNN that incorporated methods such as Principal Component Analysis (PCA), low variance and high correlation filters, nonlinear convolution layers, ensemble learning, bagging, batch normalization, and optimization algorithms including Stochastic Gradient Descent (SGD), Adagrad, RMSprop, Adam, and Particle Swarm Optimization (PSO). The performance was evaluated using metrics such as accuracy, precision, recall, and F-measure. Without optimization, the precision was 0.76; however, after extensive testing of various algorithmic combinations and synthetic data augmentation, the best configuration achieved a precision of approximately 0.98. These findings demonstrate that, with carefully selected deep learning and optimization strategies, EMG signals can be processed in near real-time, thereby significantly reducing the impact of mobility limitations.