Natural disasters such as cyclones, tsunamis, and floods frequently impact coastal regions, often leaving behind large-scale destruction and human casualties. Timely assessment of the affected area is critical for efficient rescue operations, yet traditional ground-based methods are slow, risky, and lack a comprehensive overview. This project proposes an autonomous aerial drone-based system for rapid post-disaster assessment and situational analysis in coastal regions. The drone is equipped with high-resolution cameras, thermal sensors, and AI-based object detection algorithms to identify injured individuals, estimate crowd density, locate debris, and assess structural damage. Real-time data is transmitted to a central command unit where it is processed to generate actionable insights — including the number of rescue personnel and medical units required, as well as priority zones for intervention. By providing a bird’s-eye view and real-time analytics of the disaster scene, the system significantly enhances the efficiency, accuracy, and speed of rescue and relief operations. The proposed approach minimizes response time, optimizes resource allocation, and ultimately contributes to saving more lives in the aftermath of coastal disasters.