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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66274
Volume:
10 |
7 |
Jul 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Applied Science and Humanities
Ajay Kumar Garg Engineering College Ghaziabad
Environmental testing plays a vital role in monitoring pollution, protecting ecosystems, and ensuring compliance with environmental regulations. Conventional environmental testing techniques often require extensive laboratory analysis, trained personnel, and significant time, limiting their effectiveness for real-time decision-making. Artificial Intelligence (AI), integrated with sensors, Internet of Things (IoT), remote sensing, drones, and cloud computing, has emerged as a transformative technology capable of improving the accuracy, speed, and efficiency of environmental testing. AI techniques including Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN) enable rapid analysis of complex environmental datasets and accurate prediction of pollution trends. This review discusses recent advances in AI-based environmental testing with applications in air, water, soil, and waste monitoring. The paper also highlights AI-enabled biosensors, satellite image analysis, environmental risk prediction, and smart pollution monitoring systems. Current challenges including data quality, explainable AI, computational requirements, ethical concerns, and regulatory issues are discussed. Finally, future research directions emphasizing Green AI, explainable AI, digital twins, and integrated environmental intelligence are presented.
KEYWORDS
Artificial Intelligence is revolutionizing environmental testing by enabling accurate, rapid, and cost-effective monitoring of environmental pollutants. Integration of AI with IoT, biosensors, remote sensing, and cloud computing facilitates real-time environmental surveillance and predictive analytics. Despite challenges related to data quality, interpretability, and governance, AI has substantial potential to improve environmental protection and sustainable resource management. Continued interdisciplinary research focusing on explainable, energy-efficient, and reliable AI systems will further strengthen environmental testing capabilities and support evidence-based environmental policy.
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Publication Date
7/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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