Wildlife conservation requires innovative solutions to tackle threats like habitat loss, poaching, and climate change. This work presents a framework leveraging the YOLO11 model for real-time detection and classification of endangered wildlife species. Utilizing the Snapshot Serengeti dataset, the approach enhances the detection of small objects and ensures high-quality training data through a preprocessing pipeline that filters out irrelevant frames. The system demonstrates scalability and efficiency, providing a valuable tool for wildlife monitoring and aiding conservation efforts through real-time tracking and informed decision making. Integration with Internet of Things (IoT)-enabled camera traps further highlights its potential for autonomous wildlife surveillance in remote areas, offering conservationists a significant advantage in proactive wildlife management. The fine-tuned YOLO11 model achieves an impressive mAP@50 of 88.2%, validating its effectiveness and robustness across diverse environmental conditions for practical deployment.