In modern agricultural systems, the prevention of animal intrusion is paramount to averting substantial damage and economic loss. This research introduces an artificial intelligence– driven animal detection mechanism employing a bespoke YOLO (You Only Look Once) model to facilitate the real-time identification of diverse animal species. The system is capable of analyzing video streams or static imagery, discerning the presence of specific fauna, and initiating an automated auditory deterrent contingent upon the species identified. Through the integration of advanced deep learning techniques and automated response protocols, the framework markedly enhances agricultural security, diminishes the reliance on constant human oversight, and constitutes an efficacious approach to mitigating wildlife-related threats. The model has been trained on a comprehensive dataset encompassing a range of animal species commonly encountered in agricultural vicinities, thereby ensuring a high degree of precision and operational reliability.