Environmental Monitoring using Machine Learning and IoT: Applications, Challenges, and Cyber security Threats

The convergence of the Internet of Things (IoT) and Machine Learning (ML) has transformed environmental monitoring, enabling real-time data acquisition, predictive analytics, and decision support for sustainable management of natural resources. IoT-based sensor networks offer continuous, low-cost, and scalable monitoring of air and water quality, while ML algorithms enhance accuracy through anomaly detection, calibration, and predictive modeling. This paper explores the applications, challenges, and cybersecurity threats associated with IoT–ML frameworks for environmental monitoring. Two case studies are presented: (i) Smart Air Quality Monitoring in Urban Cities, where low-cost IoT sensors combined with ML models such as Random Forest and LSTM provided improved forecasting of Air Quality Index (AQI), and (ii) IoT-enabled Water Quality Monitoring for Smart Agriculture, where classification and regression models supported irrigation management through predictive water safety assessment. Both cases demonstrate significant improvements in accuracy, cost-efficiency, and timeliness compared to traditional monitoring methods, but they also reveal challenges including sensor calibration, energy constraints, data imbalance, and security vulnerabilities such as spoofing, denial-of-service, and ransomware. The study underscores the importance of integrating cybersecurity frameworks with IoT–ML systems to ensure resilience, reliability, and trustworthiness. By analyzing technical, operational, and security aspects, this paper provides a holistic perspective on leveraging IoT and ML for sustainable environmental management.