Border security is a crucial aspect of national defense, especially for countries with large and sensitive international boundaries. Traditional border surveillance relies heavily on human patrols, ground sensors, and IoT-based systems, which face challenges in large-scale coverage, extreme terrains, and adverse weather. This paper presents an AI-powered border intrusion detection system using multi-temporal satellite imagery to autonomously monitor and detect suspicious activities across border regions. The proposed system integrates Sentinel-2 and Landsat-8 imagery, performs preprocessing using Normalized Difference Vegetation Index (NDVI) and Change Vector Analysis (CVA), and applies a Siamese Convolutional Neural Network (CNN) combined with a Vision Transformer (ViT) for change detection. Temporal behavior of detected anomalies is analyzed through a Long Short-Term Memory (LSTM) model to distinguish between environmental changes and potential intrusions. The system features a Streamlit based web dashboard for real-time monitoring, visual analytics, and alert generation. Experimental analysis using simulated Sentinel Hub and Google Earth Engine data demonstrates that the proposed architecture enhances detection accuracy and reduces false alarms, providing a scalable, real-time, and automated solution for border surveillance. The system achieves operational processing latency of 45 min per 200 km2 AOI with an interactive web dash- board delivering geotagged alerts with confidence scores and visual evidence. Our modular architecture provides a scalable, cost-effective solution for automated border surveillance with applications extending to environmental monitoring and infrastructure surveillance.