Sensory feedback with prosthetic limbs offers a substantial difficulty, restricting users’ capacity to interact with their surroundings properly. Existing prosthesis systems frequently lack real-time, adaptive response capabilities, restricting usefulness and user experience. This research addresses artificial intelligence (AI) as a revolutionary tool to better sensory input in prosthetic limbs, providing a framework employing neural networks, convolutional neural networks (CNNs), and reinforcement learning to improve responsiveness, accuracy, and user satisfaction. Our technique developed and compared various AI algorithms based on accuracy, latency, and adaptability criteria. Results revealed CNNs attained the maximum accuracy of 93.2% with the lowest latency at 140 ms, proving very beneficial for real-time applications. Reinforcement learning models, albeit significantly slower, displayed resilience in dynamic situations, displaying promise for tasks requiring constant learning and modification. These results imply that AI-driven feedback systems considerably increase the quality and intuitiveness of sensory experiences in prostheses, going closer to bridging the gap between artificial and biological sense. This study emphasizes the potential of AI to alter prosthetic technology, boosting freedom and sensory engagement for users. Future studies will concentrate on further improving these models and investigating hybrid techniques for greater clinical application.