Intelligent Environmental Sensing Systems: Integrating IoT, Edge Computing, and Real-Time Analytics for Environmental Monitoring

The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence. This review brings together the manner in which integration of Internet of Things (IoT) sensing, edge computing, and real-time analytics facilitates timely detection, interpretation, and prediction of the environmental conditions across the applications, such as urban air quality, watershed and coastal surveillance, industrial safety, agriculture, and disaster response. We define end-to-end architectural patterns to organize devices, edge nodes, and cloud services to satisfy latency, reliability, bandwidth, and governance constraints with emphasis on event-time processing, adaptive offloading, and hierarchical aggregation. Then we look at sensing and infrastructure foundations, emphasizing the effects of sensor modality and power autonomy, connectivity, and the practices of calibration on the practicable analytics and eventual plausibility. It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence (AI) techniques to preprocess, sensor combine, anomaly detect, and short-horizon forecast, with a focus on edge-deployable models, quantification of uncertainties, and query resistance to drift and domain shift. Lastly, we address the realities of deployment that condition operational success, such as lifecycle engineering, provenance-aware data management, security and privacy risks, ethical governance, and evaluation methodologies, which place end-to-end latency and field generalization as a priority. This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework, show open areas of research directions, and provide practical recommendations on how to design trustworthy, scalable, and sustainable environmental monitoring systems.