Species extinction and imbalance in their ecosystem are some major threats to wildlife preservation. The major reasons for this are illegal activities like purloining, animal killings, and theft of sandalwood trees. Detection of such incidents using traditional monitoring systems is inefficient and time-consuming. The authors propose an automated solution using deep learning for wildlife conservation. The proposed method employed a You Only Look Once version 8 (YOLOv8) object detection model to accurately identify suspicious activities. The method uses live camera feeds to detect the presence of humans or vehicles in restricted forest areas. The proposed YOLOv8 model exhibits high performance with an accuracy of 98.1% and a processing time of 22 ms per frame. A user-friendly interface has been developed to display real-time detections. The interface also generates alerts to the concerned officials and helps authorities to take immediate action. The authors also com- pared the performance of the model with other state-of-the-art detection models. The comparative results show improved accuracy and speed for the proposed model. The proposed YOLOv8 model aims to improve wildlife conservation by combining deep learning with real-time monitoring. This leads to better coordination between communities and law enforcement.