Human–elephant conflict is a major concern in regions where human settlements and agricultural lands are located near elephant habitats. Traditional monitoring methods often depend on manual observation, which can delay the identification of elephant intrusions and the delivery of warnings. This paper presents ElephantGuard AI, a real-time elephant detection and early-warning system based on deep learning. The proposed system uses the YOLO11n object detection model to identify elephants from camera images or video frames and integrates the detection results with a Flask-based backend, camera metadata, detection history, map visualization, and notification services. The trained model achieved a precision of 96.39%, recall of 95.34%, mean Average Precision at an Intersection over Union (IoU) threshold of 0.5 (mAP@0.5) of 98.16%, and mAP@0.5:0.95 of 79.34% on the reported validation evaluation of 2,473 images containing 3,542 elephant instances. These results indicate promising detection performance for the proposed application. The system records detection events with available camera and location information, displays monitoring results through a dashboard, and supports configurable alerts to help authorized users respond to potential elephant intrusions. The proposed approach aims to improve wildlife monitoring and supportearly-warning activities near vulnerable human settlements. Further testing on a separate held-out test dataset and end-to-end alert delivery evaluation are required to establish real-world performance.

A. Sherill, Subashini C, "ElephantGuard AI: Elephant Intrusion Detection and Alert System" International Journal of Scientific Research and Engineering Development, V9(5): Page(1883-1892) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.