Disaster management requires rapid, accurate decision-making based on diverse and often unstructured data sources. Machine learning (ML) algorithms, particularly Naïve Bayes and Decision Trees, offer effective computational frameworks for predicting, classifying, and managing disaster-related information. This paper presents an architectural approach for applying these algorithms in disaster management systems. The Naïve Bayes model provides a probabilistic classification mechanism suitable for early warning systems and risk assessment, utilizing historical and real-time data to predict disaster likelihood. In contrast, Decision Trees offer a hierarchical decision-making structure that aids in resource allocation, damage estimation, and response prioritization. The proposed architecture integrates data collection, preprocessing, model training, and decision-support modules to enhance situational awareness and response efficiency. Comparative analysis indicates that combining both algorithms can improve accuracy, interpretability, and computational speed. The study emphasizes the role of ML-driven architectures in enabling proactive disaster management, supporting both predictive analysis and adaptive response strategies.
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