With the rapid rise of edge AI devices in applications like smart homes, healthcare, autonomous vehicles, and wearable technologies, there is an urgent need for efficient and adaptive neural networks that can process natural language in decentralized environments. Traditional NLP models, built for cloud environments, struggle with the limited resources of edge devices. This research presents a novel Self-learning Neural Network (SLNN) architecture tailored for edge AI applications, which adapts its parameters in real-time based on incoming data. Unlike traditional models that rely on static architectures and require cloud resources for periodic updates, our system enables continuous learning and adaptation directly on edge devices. Extensive experimentation demonstrates that the self-learning architecture significantly improves NLP task accuracy, reduces latency, and optimizes energy efficiency on edge devices, offering a promising solution for the future of edge NLP. Results show a 20–30% reduction in latency and 35% increase in energy efficiency compared to conventional models.