Myoelectric prostheses have the potential to significantly improve the lives of individuals with upper limb amputations; however, challenges in control precision and signal interpretation persist. This article explores the application of Deep Q-Learning (DQN) with electromyographic (EMG) signals to optimize prosthetic control. The DQN agent optimizes prosthetic movements by integrating EMG signals and motor position data, training on a dataset collected from 12 participants using a surface EMG armband and a flexion glove. Additionally, a comparative analysis is conducted between Deep Q-Learning (DQN) and Reinforcement Learning (RL) to evaluate their effectiveness in interpreting EMG signals and controlling prosthetic devices. The results indicate that the DQN-trained agent achieved a 78% success rate in controlling the prosthesis, while the RL-trained agent achieved a higher accuracy of 86%. Despite the challenges associated with DQN’s sensitivity to hyperparameter tuning and the complexity of mapping EMG signals to motor actions, the adaptability of DQN showcases its potential for ongoing refinement. This adaptability suggests that DQN is well-suited for long-term use, especially in more complex or dynamic environments, with the goal of achieving natural hand movements and significantly enhancing the quality of life for prosthesis users.