Artificial Intelligence in Environmental Research: Applications, Challenges and Future Directions A Narrative Review
DOI:
https://doi.org/10.33003/fjs-2026-1019-6188Keywords:
Artificial intelligence, Machine learning, Deep learning, Environmental monitoring, Climate change, Biodiversity conservation, Remote sensing, SustainabilityAbstract
Climate change, biodiversity loss, and pollution are producing environmental data whose volume, velocity, and complexity increasingly surpass the capabilities of conventional analytical methods. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is being rapidly adopted to analyse these data; however, evidence regarding its performance remains dispersed across disciplines. This narrative review synthesises peer-reviewed literature identified through structured searches of Scopus, Web of Science, and Google Scholar (1997–2026), selected according to explicit inclusion criteria and organized thematically. The review examines AI applications in four domains: climate science (model emulation, downscaling, precipitation nowcasting, and methane detection); ecology and biodiversity conservation (species identification, habitat mapping, and wildlife-trade surveillance); pollution and waste management (air-quality forecasting, plastic detection, and materials design); and water resources (water-quality, streamflow, and flood forecasting). Across these domains, the most robust evidence is found in data-rich settings where AI models have been benchmarked against established physical or statistical methods. Conversely, many other applications, particularly in the Global South, remain at the proof-of-concept or simulation stage. Persistent limitations include scarce and spatially biased training data, limited interpretability, weak transferability between regions, and the energy and carbon costs associated with training large models. Additionally, concerns regarding equity, privacy, and human rights are emerging. This review concludes that AI is most valuable as a complement to, rather than a replacement for, process-based understanding and field observation. It identifies local data infrastructure, independent validation, interpretable and energy-efficient models, and equitable research partnerships as key priorities.
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