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Human–wildlife Conflict Is A Growing Concern, Particularly In Regions Where Wild Animals Intrude Into Agricultural Fields, Residential Areas, Or Roadways, Leading To Significant Crop Losses, Property Damage, And Threats To Human Safety. Traditional Animal Monitoring Methods, Such As Manual Patrolling And Camera Trapping, Are Time-consuming, Labor-intensive, And Often Fail To Provide Real-time Alerts. With Advancements In Computer Vision And Deep Learning, Object Detection Models Have Shown Great Potential In Addressing These Challenges. This Study Proposes An Advanced Wild Animal Detection And Alert System Using The YOLOv5 Model, A State-of-the-art Deep Learning Framework Known For Its High Accuracy And Fast Detection Speed. The System Is Designed To Identify And Classify Various Wild Animal Species From Live Video Streams Or Camera Trap Images In Real Time. Once Detection Occurs, An Automatic Alert Mechanism Is Triggered Through IoT-based Communication Channels (e.g., SMS, Alarms, Or Mobile Notifications) To Warn Farmers, Forest Officials, Or Nearby Residents. The Integration Of YOLOv5 With IoT Technologies Ensures Timely Response, Reduces Human–animal Conflict, And Enhances Both Community Safety And Wildlife Conservation Efforts. Experimental Evaluations Demonstrate That The Proposed System Achieves High Precision And Recall Rates While Maintaining Low Latency, Making It Suitable For Deployment In Real-world Environments.