Artificial intelligence-driven solutions for mitigating human–wildlife conflict in biodiversity hotspots

Biodiversity hotspots are biologically rich yet highly threatened regions that play a critical role in global conservation but often serve as epicentres of human-wildlife conflict (HWC). HWC poses major conservation and development challenges, undermining both human livelihoods and wildlife protection efforts. Artificial intelligence (AI) offers transformative tools for mitigating HWC by enhancing monitoring, prediction, and decision support. Through systematic searches of peer-reviewed and grey literature, this review analyzed 105 studies (1990-2025) from 163 screened sources, revealing that AI improved HWC monitoring (65%), predictive accuracy (47%), and community engagement (39%). AI-driven technologies such as machine learning, deep learning, and computer vision enable conservationists to process large datasets, automate species identification, and make real-time decisions. Integrated platforms like Earth Ranger and the Spatial Monitoring and Reporting Tool (SMART) use AI to manage data from rangers, camera traps, drones, and patrol logs, providing situational awareness and strategic planning tools. Furthermore, remote sensing, Geographic Information Systems (GIS), and participatory data integration offer multi-layered insights for mapping HWC zones, tracking wildlife movement, and modelling species distribution. This review highlights the application of AI in conflict detection, community engagement, and decision support while addressing challenges, limitations, and ethical concerns. It also underscores the importance of policies and future research to integrate AI with local knowledge systems, participatory governance, and adaptive conservation strategies. Overall, AI advancements are transforming HWC surveillance and enabling more proactive, equitable, and sustainable biodiversity conservation efforts worldwide.