Between 2000 and 2025, the EM-DAT database recorded over 16,497 natural disaster events affecting billions of people and generating trillions in economic losses across 220+ countries. Traditional disaster management systems focus primarily on historical reporting and isolated hazard analysis, limiting proactive preparedness and cascading risk assessment. This paper presents the EM-DAT Natural Disaster Analytics Platform, integrating Azure SQL, Streamlit, sequence mining, Markov-chain transition analysis, and XGBoost multiclass classi-fication to analyze multi-hazard relationships and estimate next-stage disaster transitions across six analytical modules. Evaluated on 16,497 records spanning 2000–2025, the platform achieves 76.4% accuracy and a weighted F1-score of 0.74 under a 30-day cascading window, outperforming Random Forest and Naive Bayes baselines. Results are contingent on EM-DAT data quality and the chosen cascading window definition. The frame-work identifies historical multi-hazard sequence co-occurrence patterns and supports evidence-based disaster preparedness and resilience planning.