To protect people and forestry workers from potential dangers of wild animals, this study presents an intelligent warning system that uses deep learning to track animals in real time. A hybrid approach is suggested, where VGG-19 serves to detect important features and Bi-LSTM manages the temporal sequence. The VGG-19 network extracts spatial details from each video frame and the Bi-LSTM tracks how animals move, allowing them to be identified and their trajectories to be drawn with accuracy. To strengthen and broaden how well predictions work, an ensemble method unites a CNN with a BiGRU. In real-world live video surveillance, the system maintains high success at detecting people. With the help of Bottle to create the interface, the site makes it possible for people to monitor real-time reports, identify animals involved and locate events on a map. Experiments prove that our approach can support wildlife surveillance, early warning and conflict prevention with wild animals bordering forests.