Abstract: Wildlife poaching and habitat destruction pose significant threats to biodiversity, leading to the rapid decline of endangered species. This paper presents a sustainable, AI-driven Internet of Things (IoT) surveillance framework for real-time wildlife monitoring and poaching prevention. The proposed system integrates thermal imaging, acoustic sensors, and unmanned aerial vehicles (UAVs) with convolutional neural networks (CNN) for automated detection and classification of potential threats. Data is transmitted via low-power wide-area networks (LPWAN) to cloud servers for centralized analytics, alert dissemination, and decision-making. The approach focuses on energy efficiency, cost-effectiveness, and scalability to remote forest reserves. Simulation and prototype evaluations demonstrate a detection accuracy of over 94% for human intrusions and an average
latency of under 2 seconds for alert transmission, making it a viable solution for large-scale wildlife protection.
Keywords: Artificial Intelligence, Internet of Things, Wildlife Protection, Smart Surveillance, Thermal Imaging, Poaching Detection, Convolutional Neural Network, Sustainability.

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