Sudden surface anomalies—ranging from earthquakes and floods to wildfires and industrial accidents—pose escalating threats to ecosystems and societies worldwide. Real-time remote sensing has emerged as a transformative approach for monitoring and early warning of such abrupt events, driven by recent advances in satellite constellations, on-orbit artificial intelligence, and multisource data fusion. This review systematically synthesizes the global landscape of sudden surface anomalies, elucidates their spatiotemporal patterns and underlying drivers, and critically assesses the capabilities and limitations of current remote sensing technologies for rapid detection and assessment. We highlight the shift from traditional, latency-prone processing pipelines toward integrated systems that leverage edge computing, lightweight deep learning models, and in-orbit data fusion to enable timely and automated anomaly detection. Key technical challenges are identified—including real-time atmospheric correction, model deployment under severe on-orbit resource constraints, and robust multihazard identification across heterogeneous sensor platforms. We further discuss the blueprint for next-generation systems, advocating for constellation-scale coordination, adaptive sensing, and seamless integration from detection to decision support. By bridging technical innovations with operational needs, we outline a pathway toward resilient, scalable, and intelligent remote sensing networks capable of providing actionable insights for disaster mitigation and environmental management. This review not only frames the state of the art but also charts the course for future research and system development in real-time remote sensing for sudden surface anomaly monitoring and early warning.