AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management.

Global biodiversity decline demands advanced technological solutions. This review synthesizes artificial intelligence (AI)-driven methodological innovations across data, algorithmic, and system layers for biodiversity conservation. At the data layer, multimodal sensing technologies including environmental DNA, acoustic monitors, and satellite imagery, coupled with data fusion techniques, enhance monitoring resolution. At the algorithmic layer, models ranging from convolutional neural networks to Transformers and reinforcement learning enable species identification, habitat assessment, and conservation optimization. At the system layer, federated learning, digital twins, and edge computing build scalable, secure conservation platforms. These advances restructure conservation practice from reactive monitoring toward proactive, predictive management across real-time individual tracking, precise habitat assessment, proactive threat detection, and optimized conservation prioritization. Despite challenges of data bias, algorithmic opacity, and socio-ethical tensions, responsible AI development under interdisciplinary governance offers a critical pathway toward achieving global conservation targets.