Artificial Intelligence for Environmental Monitoring and Pollution Prediction: Advances, Applications, Challenges, and Future Directions

Abstract

Artificial intelligence (AI) is rapidly transforming environmental monitoring by enabling the analysis of complex, high-volume, and heterogeneous environmental data for timely pollution detection, prediction, and decision-making. This review examines recent advances in the application of AI, including machine learning, deep learning, ensemble learning, and emerging hybrid approaches, to environmental monitoring and pollution prediction. It synthesizes evidence on the use of AI for monitoring and forecasting air pollution, water contamination, soil degradation, industrial emissions, solid waste, and other environmental hazards using data from ground-based sensors, remote sensing, Internet of Things (IoT) devices, satellite imagery, and environmental databases. The review highlights the growing effectiveness of algorithms such as random forests, support vector machines, artificial neural networks, convolutional neural networks, recurrent neural networks, and transformer-based models in identifying pollution patterns, predicting pollutant concentrations, detecting anomalies, and supporting early-warning systems. Despite these advances, significant challenges remain, including limited and inconsistent environmental datasets, sensor uncertainty, data heterogeneity, model interpretability, computational requirements, transferability across geographical contexts, and concerns regarding data governance and responsible AI. The review further identifies opportunities arising from explainable AI, edge computing, multimodal data integration, digital twins, federated learning, and real-time AI-enabled environmental decision-support systems. Overall, AI offers substantial potential to improve the accuracy, scalability, and timeliness of environmental monitoring and pollution prediction. Future research should prioritize transparent, generalizable, energy-efficient, and context-sensitive AI frameworks that integrate technological innovation with environmental science, regulatory requirements, and sustainable environmental management.