Global warming, driven by the rising concentration of greenhouse gases (GHGs), demands innovative, data- driven approaches to assess emission vulnerability at regional scales. This study developed a novel framework utilizing an unsupervised Convolutional Autoencoder (CAE) deep learning model combined with multi-sensor satellite data to map GHG emission vulnerability. The framework integrated nine environmental indicators, including tropospheric gases, land surface temperature, vegetation cover, anthropogenic proxies, and elevation, all sourced from freely accessible remote sensing platforms. The CAE model effectively captured complex spatial patterns and reduced high-dimensional inputs into 128 latent features, enabling vulnerability assessment without requiring labeled training data. Results indicated that southern coastal regions, particularly Denpasar and Badung, exhibited the highest vulnerability due to dense urbanization and tourism-related activities. Based on zonal statistics, 11.31% of local administrative zones were identified as having high to very high vulnerability, while 18.72% were classified as moderate, and 69.97% as low to very low. The most vulnerable areas were concentrated along the southern coastline, known as a hub for tourism and economic activity, with additional pockets of vulnerability found in several northern coastal zones. These findings demonstrate the capacity of unsupervised deep learning to detect emission hotspots and spatial variability, particularly in data-limited environments. The integration of scalable algorithms with open- access satellite data allows for rapid, cost-efficient assessments to inform evidence-based climate planning and mitigation strategies. This study introduces a practical and transferable approach for spatial quantification of GHG vulnerability, offering actionable insights for advancing global climate policy and supporting the Sustainable Development Goals.