The landscape of distributed computing is undergoing a profound transformation, driven by the convergence of Artificial intelligence (AI), the Internet of Things (IoT), and edge computing. combined together, these technologies are developing into intelligent systems that are capable of perceiving their environment, making autonomous decisions and act with precision. Although they provide scalability, traditional cloud-based IoT architectures struggle with high latency, network congestion, and concerns about privacy [1][2]. By performing data processing and intelligence closer to the source, edge computing aims to address these inherent problems, facilitating real-time analysis and prompt decision-making [3]. These edge-enabled IoT systems show promising possibilities in contextual awareness, adaptive optimization, predictive maintenance, and intelligent automation across highly diverse networks when augmented with Artificial Intelligence [4][5]. To quantitatively evaluate the performance of edge-enabled Intelligent systems, we conducted a simulated ECG monitoring experiment comparing cloud-only and edge-hybrid deployments. We extend beyond a review by introducing a heuristic Efficiency–Effectiveness Score (EES), as a consolidated metric for assessing system performance under multiple operational constraints to quantify trade-offs between latency, energy, and accuracy. Results indicate that edge-hybrid deployment significantly reduces latency and energy usage while maintaining high accuracy. Practical applications in healthcare, industrial automation, and autonomous mobility are discussed through case studies, while future research directions highlight promising opportunities for energy-efficient, secure, and semantically aware edge intelligence ecosystems.