The increasing availability of high-resolution satellite imagery has opened new avenues for monitoring global activities, including defense and security operations. Identifying anomalous patterns in satellite data can provide early indicators of emerging military activities, enabling proactive decision-making and strategic planning. This paper explores the application of deep learning techniques for anomaly detection in satellite imagery, focusing on convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs) to capture spatial and temporal irregularities. We propose a framework that preprocesses and fuses multi-spectral satellite data, detects deviations from baseline patterns, and highlights areas of potential military significance. Experimental evaluations demonstrate the model’s capability to identify unusual deployments, infrastructure changes, and movement patterns with high accuracy. Challenges such as limited labeled data, environmental variability, and adversarial patterns are discussed. The study concludes by highlighting future directions for scalable, real-time, and interpretable deep learning models for geospatial security intelligence.