This paper explores the integration of advanced AI-driven techniques into Natural Disaster Management (NDM), emphasizing real-time semantic visual analysis and social media text analytics for disaster response. It delves into the use of State-of-the-Art (SotA) Deep Neural Networks (DNNs) for critical tasks such as person and vehicle detection, visual privacy preservation, and georegistration. Specific methods, including YOLOv6 for person detection in flooded regions, PSPNet for flood segmentation, and I2I-CNN for fire region segmentation, are highlighted for their accuracy and real-time capabilities. Visual privacy challenges are addressed using Privacy via Adversarial Reprogramming (PAR), which obscures sensitive data while maintaining model functionality. In the domain of social media analytics, this paper examines sentiment and semantic text analysis using BERT-based models and multilingual Large Language Models (LLMs) like BERTopic, enabling real-time insights from geo-referenced posts. The study also discusses overcoming data scarcity through semi-supervised training and the potential of geovisual analytics to enhance situational awareness and resource allocation. Collectively, these approaches demonstrate the transformative potential of AI in facilitating efficient disaster response and management.