This paper presents an efficient CNN-based disaster event classification system using UAV-aided images for emergency response applications. Natural disasters are unpredictable and can have long-term consequences due to inadequate disaster management strategies. To mitigate the effects of natural hazards, disaster management teams need to act quickly and effectively. Remote areas, such as forests, rural regions, oceans, and other hard-to-reach locations, are at higher risk of receiving poor aid and response due to lack of communication and connectivity. The paper proposes using UAVs/drones for surveillance and monitoring in these areas. Drones can monitor the situation, capture images, and send them to a base station for analysis. The paper introduces a deep learning model based on feature concatenation for disaster classification, which can be deployed at the base station and receive images from UAVs. The proposed model is efficient and achieves higher accuracy compared to leading CNN models and related works. The model is designed to classify disasters before the actual management protocol begins, allowing for more effective mitigation and management based on the disaster's nature. The model is computationally affordable, easy to implement, and generates rapid results. The paper also discusses related works, including the use of UAV images for disaster classification, transfer learning, and semantic segmentation techniques. The proposed framework is evaluated on a dataset of disaster images, and the results show high accuracy and precision. The paper concludes that the proposed model is a promising solution for disaster classification using UAV-aided images.This paper presents an efficient CNN-based disaster event classification system using UAV-aided images for emergency response applications. Natural disasters are unpredictable and can have long-term consequences due to inadequate disaster management strategies. To mitigate the effects of natural hazards, disaster management teams need to act quickly and effectively. Remote areas, such as forests, rural regions, oceans, and other hard-to-reach locations, are at higher risk of receiving poor aid and response due to lack of communication and connectivity. The paper proposes using UAVs/drones for surveillance and monitoring in these areas. Drones can monitor the situation, capture images, and send them to a base station for analysis. The paper introduces a deep learning model based on feature concatenation for disaster classification, which can be deployed at the base station and receive images from UAVs. The proposed model is efficient and achieves higher accuracy compared to leading CNN models and related works. The model is designed to classify disasters before the actual management protocol begins, allowing for more effective mitigation and management based on the disaster's nature. The model is computationally affordable, easy to implement, and generates rapid results. The paper also discusses related works, including the use of UAV images for disaster classification, transfer learning, and semantic segmentation techniques. The proposed framework is evaluated on a dataset of disaster images, and the results show high accuracy and precision. The paper concludes that the proposed model is a promising solution for disaster classification using UAV-aided images.