Predicting Natural Disasters Using Artificial Intelligence And Machine Learning: A Review Of Methods, Data Sources And Early Warning Architectures

Floods, earthquakes, cyclones, landslides and droughts cause heavy loss of life and property, and their impact depends strongly on how early communities are warned. Conventional physics-based forecasting remains essential, but it struggles to exploit the volume and variety of data now produced by satellites, ground sensors and historical archives. This paper reviews how Artificial Intelligence (AI) and Machine Learning (ML) are being used to estimate disaster risk and support early warning. It surveys data sources, including rainfall, river levels, seismic waveforms, reanalysis fields and satellite imagery, and examines models ranging from random forests and support vector machines for susceptibility mapping to long short-term memory networks for flood forecasting, convolutional and transformer networks for earthquake detection, and graph-based models for medium-range weather prediction. A five-layer architecture for an AI-assisted early warning system is proposed, linking data ingestion, preprocessing, hazard-specific models, risk fusion and alert dissemination. The paper identifies research gaps such as data scarcity in ungauged regions, limited uncertainty reporting and the weak connection between forecasts and community action. It concludes that AI predictions should be treated as decision support that complements, rather than replaces, physical models and expert judgement.