The swift advancement of the Internet of Things (IoT) has transformed the design approach from relying on cloud-based intelligence to decentralized, self-governing systems able to make decisions in real-time. This document introduces an advanced IoT framework that closely incorporates embedded Artificial Intelligence (AI) into edge and device-level elements to improve system independence, reactivity, and scalability. The suggested framework utilizes efficient AI models, enhanced hardware accelerators, and edge–cloud cooperation to facilitate context-aware sensing, adaptive management, and self-learning features in diverse IoT settings. The architecture enhances latency performance, energy efficiency, and data privacy by minimizing dependence on constant cloud connectivity. Experimental assessments show marked enhancements in processing speed, accuracy of autonomous operations, and overall system robustness during changing workloads. The research emphasizes the revolutionary capability of integrated AI in influencing upcoming IoT environments and offers design recommendations for implementing scalable, smart, and self-regulating IoT systems
 

IoT Architecture; Embedded AI; Edge Computing; Autonomous Systems; and Lightweight Machine Learning.