<![CDATA[Coastal areas are increasingly vulnerable to abrasion, flash floods, and seawater intrusion due to climate change and human activities. Addressing these hazards requires an integrated early warning system that enables timely and adaptive disaster mitigation. This study integrates landscape analysis, Mobile GIS technology, and the Ant algorithm into a unified early warning framework. Landscape analysis identifies geomorphological patterns and coastline changes. Mobile GIS supports real-time spatial data acquisition, while the Ant algorithm optimizes evacuation routes, sensor placement, and hazard prediction through agent-based modeling. The system incorporates spatial, topographic, oceanographic, and socio-economic data processed through spatial and computational methods. Validation using historical records and field data shows enhanced hazard detection, more efficient evacuation planning, and quicker response times. The Ant algorithm adapts routes in real time based on environmental changes. Sensor deployment is optimized for high-risk zones. Mobile GIS ensures continuous updates and spatial visualization. Real-time processing supports rapid decision-making and threat modeling. These integrated components demonstrate a strong potential to build a resilient, data-driven early warning system for coastal communities. In conclusion, the proposed system offers a precise, adaptive, and scalable approach to improve disaster preparedness in vulnerable coastal regions. Keywords: early warning system, Mobile GIS, ant algorithm, landscape analysis, coastal disaster mitigation]]>
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