International Journal of Multidisciplinary Engineering Sciences
Peer Reviewed Open Access Journal
Peer Reviewed Open Access Journal
Environmental pollution and climate change have become major global concerns affecting human health, biodiversity, agriculture, and industrial sustainability. Traditional environmental monitoring systems often rely on manual observation methods and isolated sensing mechanisms, which are inefficient for real-time analysis and predictive decision-making. The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has introduced intelligent environmental monitoring solutions capable of continuous sensing, automated analysis, and early hazard prediction. This paper presents an AI-based environmental monitoring framework integrating IoT sensors, cloud computing, and machine learning algorithms for real-time monitoring of environmental conditions. The proposed system collects data related to air quality, temperature, humidity, water quality, noise levels, and gas emissions using IoT-enabled smart sensors. Machine learning techniques are employed to analyze environmental patterns and predict pollution levels and hazardous conditions. Cloud infrastructure facilitates centralized data storage, remote monitoring, and intelligent analytics. Experimental analysis demonstrates improved monitoring accuracy, efficient anomaly detection, and faster environmental response compared with conventional monitoring systems. The proposed framework offers a scalable, cost-effective, and intelligent solution for smart cities and sustainable environmental management.
Environmental Monitoring, Artificial Intelligence, Internet of Things, Machine Learning, Smart Sensors, Pollution Detection, Smart Cities, Predictive Analytics
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