Abstract
Biodiversity conservation is a global priority because it directly affects ecosystem resilience and sustainable development. Traditional biodiversity monitoring methods are often constrained by high labour intensity, limited spatial and temporal coverage, and the need for expert interpretation, which complicates timely assessment under climate change and increasing anthropogenic pressure. This study examines how artificial intelligence and machine learning methods can support biodiversity assessment and ecosystem resilience monitoring. The paper focuses on machine learning approaches applicable to satellite imagery, acoustic recordings, camera-trap images, UAV data, and ecological databases. Particular attention is given to computer vision, deep learning, clustering, time-series analysis, and big data processing. The proposed conceptual architecture integrates data collection, preprocessing, analytical modelling, visualization, and decision-support modules. The results of a prototype demonstration based on simulated ecological indicators show that AI-based methods can identify biodiversity patterns, support the detection of ecosystem changes, and assist in environmental risk assessment. The findings suggest that artificial intelligence may complement traditional field-based monitoring by improving data processing speed, scalability, and reproducibility. At the same time, the study emphasizes the need for validation using empirical ecological datasets and interpretable models before practical implementation in conservation management.