Abstract

Environmental monitoring is essential for understanding ecosystem dynamics, detecting environmental change, and supporting evidence-based decision-making for sustainable development. Increasing pressures from climate change, urbanization, deforestation, pollution, and biodiversity loss have intensified the demand for monitoring approaches that are accurate, scalable, and capable of handling large and complex datasets. In this context, machine learning (ML) has emerged as a powerful analytical tool due to its ability to model non-linear relationships, process high-dimensional data, and generate reliable predictions. This paper presents a narrative review of ML applications in environmental monitoring, outlining major paradigms: supervised, unsupervised, semi-supervised, and reinforcement learning, and examining their relevance across key environmental domains. Core application areas reviewed include land use and land cover mapping, climate and weather analysis, air and water quality assessment, and biodiversity monitoring. The review also evaluates common environmental data sources such as remote sensing imagery, in situ sensor networks, environmental databases, and citizen science datasets, as well as emerging data processing platforms. In addition, it discusses challenges related to data quality, model interpretability, generalization across regions, and computational constraints, and highlights future directions emphasizing data integration, explainable artificial intelligence, and operational deployment for sustainable environmental management.

Keywords:Machine learning, Environmental monitoring, Remote sensing, Environmental data, Artificial intelligence, Sustainability