Integrating Artificial Intelligence in Environmental Monitoring: A Paradigm Shift in Data-Driven Sustainability

Environmental monitoring is essential for understanding and minimizing human impact on ecosystems. Traditional methods like manual sampling and laboratory testing, while accurate, are often costly, time-consuming, and difficult to scale, especially in low-resource settings. Artificial intelligence (AI) is increasingly addressing these limitations by enabling automated data collection, real-time analysis, and predictive modeling. Techniques such as machine learning (ML) and deep learning (DL) are being applied to monitor air and water quality, track climate patterns, and support biodiversity efforts. Hybrid AI models further improve accuracy by integrating various analytical approaches. Key applications include species identification, habitat assessment, wildlife tracking, and anti-poaching, utilizing tools such as drone imagery, camera traps, and GPS data. This review explores the latest advancements in AI-based environmental monitoring, emphasizing technologies like explainable AI (XAI), edge computing, and the Internet of Things (IoT), which improve transparency and reduce processing costs. It also addresses ongoing challenges, including data quality, computational demands, and the need for interpretable models. By evaluating practical limitations and proposing interdisciplinary strategies, this article highlights the transformative potential of AI for sustainable environmental management. Successful implementation will depend on ethical frameworks, policy alignment, and cross-sector collaboration to fully realize AI's role in global ecological stewardship.