This study focuses on enhancing prosthesis control via the integration of deep learning models and adaptive control methods. Our work combines VGG16, CNN, and RNN architectures to increase gesture detection from EMG data, permitting precise control of prosthetic limbs. We created and trained these models using a dataset of EMG measurements related to diverse hand gestures, obtaining great accuracy and resilience in gesture categorization. The adaptive control system, including real-time feedback and a PD controller, significantly increased prosthesis responsiveness, displaying considerable gains in operational accuracy. The findings underline the potential for these sophisticated models to change prosthesis control, enabling greater user experience and usefulness. This study is innovative in its coupling of deep learning with adaptive control for prostheses, emphasizing considerable gains over prior approaches. The ramifications of this study extend to enhanced prosthetic devices with more intuitive and effective control methods.