The Immersed Intelligence can be a solid platform for the development of smart environmental monitoring systems to monitor dynamic environments that change rapidly over time. Environmental monitoring applications require a sensor network that can sense and collect their surroundings, perform computations, make decisions, and transmit information over the Internet. An intelligent environment monitoring system handles the effective utilization of sensors to ensure efficient sensing of exterior environment factors. Such network has the potential to measure not only environmental pollutions but the fine grain environmental parameters of an individual’s local micro-climate, which is crucial to improve human living quality and assess the environmental impact on human health. Developing intelligent sensing devices provides a collection of techniques to enhance the ability of sensor network to adapt to the dynamic behavior of its surrounding environment and operate efficiently. As the environmental information from numbers of sensor arrays becomes larger and more complicated, there is a need for systems to model and predict future scenarios. Presenting the relevant and logical information with data mining and deep learning techniques are necessary to explore the versatility of environmental sensing data. For example, significant effort has been focused on new cloud-based web platforms that enable the aggregation, analysis, and real-time display of unprecedented streams of environmental data. Innovations in AI, 3D object-recognition algorithms, and genetic learning have been applied to the study and administration of ecological processes. Collectively, these developments have dramatically increased scientists’ ability to assess significant changes in environmental monitoring.