Rammed earth sites are vital heritage structures with considerable historical and cultural value. However, they face severe degradation due to natural erosion and human activities in arid Northwest China. Current methods for their extraction and monitoring remain limited, particularly in integrating multi-source remote sensing with advanced machine learning for precise localization and predictive analysis. In this study, we aimed to address these gaps by developing an accurate boundary extraction framework for rammed earth sites using multi-source remote-sensing data, and simulating their future spatiotemporal evolution to support proactive conservation. We compared three machine learning approaches: Object-Based Image Analysis combined with Convolutional Neural Networks (OBIA-CNN), Maximum Entropy Model-based Discrete Particle Swarm Optimization (MEDPSO), and U-Net-based semantic segmentation. OBIA-CNN outperforms MEDPSO and U-Net, achieving superior accuracy (OA = 97.46%, Kappa = 0.95) with strong anti-interference and generalization capabilities, effectively minimizing salt-and-pepper noise and preserving structural continuity. While achieving a high recall (0.9731), U-Net exhibited boundary expansion and over-segmentation, limiting its precision in delineating fine archaeological features. We applied the Markov-PLUS model to simulate land-use changes around four representative sites from 2023 to 2056 under natural scenarios, incorporating environmental and socioeconomic drivers. The model indicated critical transitions in land cover that threaten site preservation, enabling the identification of high-risk zones. This study provides an integrated framework that bridges high-precision site extraction with spatiotemporal simulation, offering a scientific basis for the sustainable conservation of rammed earth heritage in arid environments.