AI-Driven Internet of Things for Environmental Monitoring and Climate Intelligence

Abstract

Environmental degradation, climate change, air pollution, water contamination, and extreme weather events have increased the demand for intelligent and continuous environmental monitoring systems. The integration of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) offers new opportunities for collecting, processing, and interpreting environmental data at unprecedented scales. This review investigates AI-driven IoT applications for air-quality monitoring, water-quality assessment, soil monitoring, weather prediction, wildfire detection, flood forecasting, and climate-risk assessment. The study examines the use of IoT sensors, wireless sensor networks, satellite observations, drones, and edge devices for real-time environmental data acquisition. Various ML and deep learning methods used to analyze environmental data are reviewed, including regression algorithms, decision trees, neural networks, convolutional neural networks, and recurrent neural networks. Challenges related to sensor reliability, data heterogeneity, network connectivity, energy consumption, scalability, and model accuracy are discussed. The review also considers the role of edge AI, cloud computing, digital twins, and autonomous sensing systems in future environmental intelligence. The study identifies important research opportunities for developing cost-effective, scalable, and intelligent environmental monitoring frameworks that can support climate adaptation and sustainable resource management.