Artificial Intelligence and Machine Learning in Environmental Monitoring: A Research Review of Methods, Applications and Case Studies from India

Authors:

Shailesh S Dongare¹

¹Department of Physics, S. G. Arts, Science and G. P. Commerce College, Shivle,

Murbad, 421 401, (M.S), India

email: shaileshdongare@yahoo.co.in

Abstract

Environmental monitoring is critical for preserving ecosystems, maintaining public health, and promoting sustainable development. The fast rise of sensor networks, satellite observations, and digital data infrastructures has resulted in massive amounts of diverse environmental data, opening up new avenues for advanced data-driven analysis. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as effective methods for transforming traditional environmental monitoring systems into scalable, accurate, and predictive frameworks.

This paper provides a detailed assessment of AI and machine learning applications in environmental monitoring, with an emphasis on Indian use cases. Air and water quality assessments, climate change modeling, biodiversity protection, pollution forecasting, and urban environmental analytics are among the most important application domains. The study examines the role of supervised, unsupervised, and deep learning techniques, including Random Forests, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, in extracting relevant insights from complicated environmental datasets. The integration of enabling technologies, such as the Internet of Things (IoT), remote sensing, Geographic Information Systems (GIS), and edge computing, is thoroughly examined. These technologies enable real-time data collecting, large-scale environmental sensing, and the efficient deployment of AI-powered models.

The article also examines system topologies and shows performance gains made by AI-based approaches against traditional monitoring methods. Furthermore, significant problems connected with AI and machine learning adoption are examined, such as data quality and availability, model generalization across regions, algorithm transparency and explainability, energy efficiency, and computational cost. Emerging solutions including explainable AI, federated learning, and energy-aware system design are explored as promising approaches to addressing these difficulties.

The analysis focuses on Indian initiatives in smart waste management, wastewater intelligence, flood prediction, and digital environmental governance, illustrating the practical application of AI-enabled monitoring systems in underdeveloped countries. Finally, new research initiatives are offered, focusing on the creation of indigenous datasets, region-specific and policy-oriented models, citizen science integration, and ethical AI deployment. Overall, the study emphasizes the critical review of AI and machine learning in developing next-generation environmental monitoring systems that enable informed decision-making and sustainable development.

Keywords: Artificial Intelligence, Machine Learning, Environmental Monitoring, Air Quality, Water Quality, IoT, Predictive Analytics.