Machine Learning for Biodiversity Conservation: A Review of Ecological Monitoring, Species Prediction, and Conservation Applications

Biodiversity loss, habitat degradation, climate change, invasive species, and unsustainable resource use have intensified the need for effective and evidence-based conservation strategies. Machine learning (ML) has emerged as a valuable approach for processing large and complex ecological datasets, identifying species, predicting species distributions, monitoring ecosystem changes, and supporting conservation decision-making. This review examines the applications, methodological developments, benefits, and limitations of machine learning in biodiversity conservation, with particular emphasis on ecological monitoring, species prediction, and practical conservation interventions. It synthesizes major approaches, including supervised learning, unsupervised learning, deep learning, computer vision, acoustic classification, remote sensing, and ensemble modelling. Evidence from previous research demonstrates that automated camera-trap analysis, bioacoustic monitoring, satellite imagery, and environmental DNA analysis can improve the efficiency and spatial coverage of biodiversity assessments. Species distribution models also support the identification of suitable habitats, climate-sensitive areas, and potential conservation priorities. Furthermore, machine learning can facilitate invasive-species detection, wildlife population assessment, habitat restoration, and protected-area management. However, its effectiveness depends on data quality, representative training datasets, model interpretability, computational resources, and collaboration between ecologists, conservation practitioners, and local communities. Ethical concerns, including sensitive species-location disclosure, surveillance risks, and unequal access to technological infrastructure, also require attention. The review concludes that machine learning can strengthen biodiversity conservation when integrated with ecological expertise, field validation, transparent governance, and long-term monitoring. Future research should prioritize geographically representative datasets, explainable models, uncertainty assessment, and conservation outcomes that can be independently evaluated.