In tribal regions where people and elephants interact, effective wildlife monitoring is crucial. This paper proposes an efficient elephant detection system that uses TensorFlow. It was trained on a large dataset of labeled elephant images and deployed through Google Colab for easy use and scalability. The model was designed for remote forest areas and improved through various deep learning structures. We evaluated and compared models using key performance metrics, including accuracy, precision, and recall, to find the best one. The final model showed quick detection, taking under one second per image, and performed well even in low light and dense forests, surpassing traditional methods. We strategically applied data augmentation and model tuning to improve generalization and reduce false detections. Google Colab allowed us to train and deploy the model cost-effectively, cutting computational costs by over 50%. Recent studies on hybrid deep neural network methods and edge computing strategies support the efficiency of real-time monitoring systems. The outcomes demonstrate that our system is scalable and dependable for tracking elephants in far-flung locations and supports long-term wildlife preservation.