EMG-based prosthetic control systems are known to suffer from muscle fatigue, electrode displacement, signal drift, and inter-subject variability issues, compromising their robustness and long-term stability. While recently developed deep learning techniques deliver highly competitive gesture recognition accuracy, most existing solutions lack adaptive learning and privacy-preserving model optimization mechanisms required for the real-world implementation. In this paper, we propose a new EMG-based adaptive prosthetic control system combining the Hybrid CNN, ViT, and LSTM architectures and applying federated reinforcement learning (FRL). The CNN component of the proposed architecture extracts local spatial information from multi-channel EMG signals, the Vision Transformer analyzes inter-channel dependencies using a self-attention mechanism, while the LSTM models the temporal dynamics of muscle activation. Additionally, the reinforcement learning algorithm constantly updates control policies according to user feedback, while federated learning allows users to collaborate on model optimization without exposing raw EMG data, thus protecting user privacy. In order to enhance the system robustness in real-world conditions, the Adaptive Muscle Signal Learning (AMSL) technique is used to counteract the adverse effect of signal drift, muscle fatigue, and electrode displacement. An EMG dataset specification, called EMGPro-2026, is introduced to establish the criteria of data acquisition, gesture recognition classes, preprocessing, and evaluation process. Based on computational assessment and simulation analysis, there is proof of concept for the proposed framework that enables high gesture classification accuracy, adaptation for personalization, and efficient learning with privacy preservation for real-time use in prosthetics. The proposed CNN-ViT-LSTM with federated reinforcement learning presents an intelligent solution for the future generation of myoelectric prosthetic control systems. Our future work will entail the validation of our framework using EMG signals recorded ethically. Because the current research provides a conceptual framework, there has been no experimental validation of the performance using human EMG datasets. However, based on the benchmarks developed from the existing state-of-the-art literature, it can be said that the suggested framework for Hybrid CNN–ViT–LSTM with Federated Reinforcement Learning is predicted to deliver approximately 94.5% gesture classification accuracy, which will be better than the conventional techniques such as CNN, CNN–LSTM, and SVM and will also increase adaptability, personalization, and privacy preservation.