The increasing deployment of Artificial Intelligence (AI) systems in real-world applications has revealed limitations of cloud-centric architectures, particularly in scenarios requiring low latency, high reliability, and efficient resource usage. Edge AI addresses these challenges by shifting data processing and model inference closer to the data source. This paper presents a conceptual analysis of Edge AI as a paradigm for real-time decision making in resource-constrained environments. The study systematizes Edge AI architectures, model optimization strategies, and deployment constraints, emphasizing trade-offs between computational efficiency, accuracy, and system robustness. Rather than proposing a specific implementation, the paper provides an analytical framework intended to guide researchers and practitioners in evaluating Edge AI solutions across industrial and societal domains.