Wildlife monitoring in remote field environments presents significant challenges owing to energy constraints, limited network connectivity, and the need for real-time processing. This study proposes a scalable framework that integrates artificial intelligence (AI) and edge computing for autonomous wildlife detection, class-based object counting in frames, distance measurement, and geolocation estimation of key European wildlife species. The system leverages deep learning-based object detection models, specifically YOLOv8m and YOLOv10m trained by the European Animal Detection Dataset, deployed on power-efficient embedded platforms such as the NVIDIA Jetson Orin Nano and Raspberry Pi 5. The experimental evaluation assesses detection accuracy, computational efficiency, and energy consumption across different hardware configurations, ensuring an optimal trade-off between performance and resource utilization. Our findings demonstrate that the proposed approach enables real-time inference while maintaining high mean average precision (mAP) and minimizing power consumption, ensuring feasibility in resource-limited field conditions. A comprehensive cost-performance analysis highlights the Jetson Orin Nano as the optimal choice for balancing accuracy and efficiency, achieving 40.1 Frame Per Second (FPS) at just 8W in 25W power mode using a TensorRT-optimized YOLO model. Meanwhile, the Raspberry Pi 5 serves as a cost-effective alternative for lightweight models. Beyond technical performance, this work underscores the transformative role of edge AI in supporting ecologists and agronomists with actionable insights for biodiversity conservation, human wildlife conflict mitigation, and sustainable ecosystem management.