Wildlife conservation increasingly relies on auto-mated monitoring systems to overcome the limitations of tradi-tional field-based observation methods, which are labor-intensive, subjective, and constrained in spatial and temporal coverage. This paper presents an AI-based animal monitoring and behavior analysis framework that integrates deep learning-based object detection, multi-object tracking, and spatio-temporal analytics for real-time wildlife surveillance. A YOLO26l detection model is employed to identify animal species from camera trap im-agery and video streams, followed by location-aware tracking to analyze movement patterns and population density. Heatmap-based visualization and statistical analysis are used to infer behavioral trends across different time intervals. Experimental results demonstrate robust detection accuracy and reliable species classification, supported by confusion matrix-based evaluation. The proposed system offers a scalable and interpretable solution for intelligent wildlife monitoring and conservation planning.