The proposed framework introduces a novel and unique approach to monitoring deforestation and detecting unauthorized tree-cutting activities by integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for spatiotemporal analysis, which enables the detection of complex temporal patterns in forest ecosystems. This system is different from traditional methods as it applies transfer learning, fine-tuning pre-trained models like ResNet and VGG, which makes it more adaptable to deforestation-specific activities. It includes multi-source data such as real-time satellite imagery and video frames and integrates Geographic Information System (GIS) tools for event detection and anomaly analysis, offering a scalable and comprehensive solution. The model is trained on datasets such as the KTH human activity dataset and Kaggle videos, and it has an accuracy of 86%, which can be improved as the dataset grows, thus showing its capability to address the challenges of forest conservation and unauthorized activity detection.