Wildfires are one of the most dangerous natural disasters, causing large-scale damage to forests, wildlife, property, and human life. Early and accurate detection plays a crucial role in minimizing these losses. In this research, an extended FireNet-CNN framework is proposed for robust, interpretable, and multi-hazard disaster detection using deep learning and Explainable Artificial Intelligence (XAI). Unlike traditional review-based approaches, this work incorporates comparative experimental validation using benchmark wildfire datasets and performance metrics including accuracy, precision, recall, and F1-score. The proposed lightweight architecture is optimized for real-time deployment on edge devices, UAVs, and surveillance systems while maintaining high detection accuracy and low computational complexity. Experimental comparison with ResNet50, YOLOv8, MobileNetV2, and transformer-based models demonstrates that the extended FireNet-CNN achieves a balanced trade-off between accuracy, inference speed, and interpretability. These advancements establish FireNet-CNN as a scalable and reliable solution for real-world disaster management and intelligent wildfire monitoring systems.