This article examines Edge Artificial Intelligence (Edge AI) as an emerging paradigm for real-time decision making in environments with limited computational and energy resources. The study highlights the limitations of cloud-centric AI architectures in latency-sensitive and reliability-critical applications and motivates the shift toward decentralized intelligence at the network edge. The paper provides a conceptual overview of Edge AI architectures, including on-device inference, edge–cloud collaboration, and hierarchical edge systems. It discusses model optimization techniques such as quantization, pruning, and knowledge distillation, emphasizing the trade-offs between computational efficiency and predictive performance. Special attention is given to reliability and robustness challenges arising from hardware constraints, environmental variability, and intermittent connectivity. The article also outlines key limitations and open research challenges, including device heterogeneity, lifecycle management, and the lack of standardized evaluation benchmarks. Overall, the study positions Edge AI as a promising approach for enabling responsive and autonomous intelligent systems while underscoring the need for careful system-level design and methodological evaluation.