International Journal of Scientific Research and Engineering Development

The increasing scale and complexity of modern networks, coupled with the rapid proliferation of Internet of Things (IoT) devices, have created new challenges for real-time monitoring and management. Conventional centralized monitoring systems struggle with latency, scalability, and bandwidth inefficiencies, limiting their ability to support dynamic and data-intensive environments. This paper proposes a cloud-integrated network monitoring dashboard that combines IoT sensing technologies with edge analytics to deliver continuous visibility, adaptive diagnostics, and predictive insights. In the proposed model, edge nodes perform local preprocessing and anomaly detection, transmitting only summarized data to the cloud to minimize network load while preserving analytical depth. The cloud layer integrates these data streams into an interactive dashboard for system-wide visualization, performance forecasting, and automated alert generation. Experimental validation across a distributed testbed demonstrated a 38% reduction in latency, 46% lower bandwidth usage, and a 92% anomaly detection accuracy compared with conventional systems. The results confirm that hybrid cloud-edge integration provides a robust solution for scalable network intelligence and operational resilience. This architecture is particularly suitable for enterprise, industrial, and smart-city networks requiring high reliability and rapid response. Future extensions may incorporate AI-based predictive analytics, blockchain-secured data transfer, and self-adaptive fault recovery mechanisms to enhance overall system robustness.