Application of Artificial Intelligence for the Protection of Natural Resources in the Indian Sundarbans: Possibilities and Challenges

The Sundarbani mangrove ecosystem-spanning the Indian state of West Bengal and the neighbouring country of Bangladesh-is the world's largest contiguous mangrove forest and a UNESCO World Heritage Site.It provides critical ecosystem services, ranging from carbon sequestration and coastal protection to the sustenance of millions of people through fisheries, agriculture, and tourism.Yet the Sundarbans faces unprecedented pressures: sealevel rise, cyclonic storms, habitat fragmentation, illegal extraction of timber and nontimber forest products, and poaching of endangered species such as the Bengal tiger and the Indian river dolphin.Conventional monitoring and enforcement mechanisms are hampered by the region's complex geomorphology, sparse infrastructure, and limited human resources.Artificial Intelligence (AI)-encompassing machine learning (ML), deep learning (DL), computer vision, natural language processing, and autonomous sensing-offers new pathways to sense, model, predict, and manage natural resources at spatial and temporal scales previously unattainable.This paper presents a comprehensive review of AIdriven tools and techniques applied to the Indian Sundarbans, evaluates their potential to enhance conservation outcomes, and critically examines technical, socioeconomic, and governance challenges that must be addressed to realize their full promise.Drawing on case studies ranging from satellitebased mangrove mapping and droneenabled wildlife monitoring to communitycentric mobile platforms for illegalfishing detection, the analysis identifies bestpractice models, knowledge gaps, and policy recommendations for integrating AI into the region's naturalresource management framework.