Wild animal detection and management are dynamic for protecting yields, reducing human-wildlife encounter, and civilizing rural care. This evaluation describes advanced technologies used to notice and monitor wildlife action, gathering on YOLOv8-based computer vision systems, Internet of Things (IoT) devices, and precision farming tools. These solutions aim to decrease crop losses, prevent attacks on forestry workers, and reduce risks from unpredicted road adventures by wild animals. The combination of drones, surveillance cameras, ultrasonic preventions, and smart applications enables real-time detecting and the creation of effective limitations to constrain animal struggle. In addition to large animal detection, precision farming systems are increasingly applied to control pest behaviour, thereby reducing dependency on chemical insect killer. Deep learning models are trained to identify animal types, recognize behaviour patterns, produce alert systems to inform farmers and local authorities. This paper distributes an entire review of present methods, equating datasets, recognition procedures, hardware claims, and disposition surroundings. Key changes in target species, device categories, exposure accuracy, and field show remain highlighted. Additionally, the study summaries current limits and organizes open research areas for future evolution, as well as better behaviour prediction, multi-modal detecting, and low power edge-based appreciation systems for real-world cultivated applications.