Abstract: The increasing frequency and intensity of natural hazards highlight the limitations of traditional disaster monitoring systems that rely on static sensors and delayed reporting. Advances in the Internet of Things and computer vision now enable distributed, real-time observation across multiple hazards. IoT networks provide fine-scale measurements of environmental and structural parameters, while vision systems using cameras, drones, and satellite imagery deliver spatial verification and automated impact assessment through artificial intelligence. When integrated, these technologies improve detection accuracy, shorten response times, and strengthen situational awareness. This review synthesizes recent global developments across three dimensions: technology, resilience, and governance. The analysis examines hybrid architectures that merge IoT and vision systems, evaluates continuity strategies such as renewable-powered microgrids and UAV-based communications, and identifies governance challenges involving interoperability, privacy, and institutional coordination. A layered conceptual framework is proposed to link sensing, analytics, alerting, and policy mechanisms. Findings reveal persistent gaps in endurance during extended outages, the need for generalizable multihazard fusion models, and the importance of ethical data governance. The synthesis provides guidance for integrating IoT-vision systems into resilient, scalable, and inclusive early warning infrastructures aligned with global sustainability and disaster-risk-reduction goals.