The coexistence of humans and wildlife has led to an increasing need for early detection and warning systems to mitigate potential dangers posed by dangerous animals. This novel approach addresses this challenge by harnessing recurrent neural networks (RNNs) for real-time detection and alerting. The system utilizes a deep learning framework to continuously analyse environmental data streams, such as live camera feeds from critical wildlife crossing areas and animals perambulate areas. This data source provides a multi-model input stream for the RNN, enabling the model to learn complex patterns associated with dangerous animal presence. When a potential threat is detected, the system triggers alerts through various communication channels such as mobile apps, mails, and sirens. The results offer a promising solution to mitigate the risks associated with wildlife hazards, fostering coexistence between humans and wildlife while safeguarding lives and the natural world.