This paper introduces a novel approach to adaptive neurofeedback therapy by integrating the Adam optimizer with Q-learning algorithms. The proposed method enhances cognitive and motor function recovery by providing real-time feedback based on patients’ brain activity. Traditional neurofeedback techniques often lack individualization and dynamic adjustment, limiting their effectiveness. Integrating reinforcement learning techniques, such as Q-learning with optimization algorithms like Adam, offers a promising solution to these limitations. Through a Python-based simulation, we demonstrate the efficacy of this approach in dynamically adjusting neurofeedback parameters to optimize therapeutic outcomes. Additionally, we apply the Adam-DQN technique to a simplified motor task, showing its potential to improve adaptive learning systems.