This study introduces an AIoT-based Animal Intrusion Detection System designed to address human-wildlife conflicts in Bhutan’s agricultural sector. The system leverages the YOLOv8 model integrated with IoT technology to detect and classify wildlife intrusions in real-time, sending notification alerts to farmers via a mobile application. The model was trained on 30,800 images of seven animal species commonly responsible for crop damage, achieving an accuracy of 95.7% during testing in a controlled lab environment. Key components of the system include a Raspberry Pi4, a camera, and ultrasonic sensors. The system shows significant potential to reduce crop losses, enhance food security, and improve rural livelihoods. However, its current limitations include a narrow dataset focused on a limited number of animal species and the lack of field testing under diverse conditions. Future improvements should prioritize expanding the dataset, refining the AI model, and conducting extensive field trials to optimize performance. This approach offers a promising solution to mitigate crop damage and support Bhutan’s predominantly agricultural communities.