The effective protection of livestock and wildlife from threats is a critical aspect of animal welfare and conservation efforts. Traditional methods often rely on constant human vigilance, which is resource-intensive and prone to error. With advancements in Artificial Intelligence (AI) and the Internet of Things (IoT), virtual monitoring offers a robust solution for real-time threat detection and proactive intervention. This project proposes the development of an integrated AI/IoT-powered system designed for the autonomous detection of specific animal threats or intruders within designated areas, coupled with an automated warning or elimination mechanism. The system will employ an array of interconnected sensors, including advanced computer vision (e.g., YOLOv8-based object detection) and potentially radar, to continuously monitor the environment for unauthorized animal presence or predatory threats. Upon detection and identification of a designated threat, the system's core processing unit, like a Raspberry Pi, will precisely calculate the target's coordinates. This data will be transmitted to an Arduino-controlled, multi-axis turret system. This turret, equipped with a calibrated laser or other specified non-lethal deterrent/elimination mechanism, will then accurately track and engage the identified threat, providing an immediate and localized response. Furthermore, anomaly detection algorithms will enhance reliability by cross-referencing sensor inputs, while a GSM module will provide real-time alerts to stakeholders upon engagement or significant events. The integration of YOLOv8 with sensor fusion has achieved high detection F1-score of over 90%. This ensures reliable real-time identification of specific animal threats. It also has a quick response time of about 2.1 seconds from detection to activation of the deterrent ensuring no attack and no loss on both sides.