Early Natural Disaster Prediction Using Machine Learning: A Comprehensive Review

In many parts of the world, climate change has caused floods, earthquakes, cyclones, wildfires, and landslides that are more frequent and violent than ever before, and human activity is making them worse through deforestation and urbanization, threatening lives, economies, and ecosystems at previously unseen levels. The fallout from such events costs billions each year and forces millions from their homes — highlighting the need for better predictive tools to improve early warning systems that can trigger timely interventions to avoid human suffering and economic destruction. Into this space, machine learning (ML), a genuinely transformational technology, is operating on ever-larger, more heterogeneous data stores (space-borne satellite images (e.g. Landsat), Internet of Things (IoT) sensors networks, meteorological weather records, seismic observatory systems, hydrological valences, etc.) to both increase the precision of forecasting and lower the time from forewarned to foreclosure. This broad review collects a range of ML techniques from traditional supervised approaches – including support vector machines (SVMs), artificial neural networks (ANNs) and decision trees – to unsupervised methods such as K-means clustering and DBSCAN for anomaly detection, through to more advanced deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) with LSTM units and transformer architectures (e.g., ViT-B-32), as well as ensemble methods, like Random Forests and XGBoost, that aggregate many predictors together for higher robustness. Based on a review of an extensive literature (e.g. Alamri (2018), Mosavi et al. (2018) on surface water flooding prediction, Belenguer et al. ), and systematic ML methodologies (Thajudeen et al. Singh et al. (2024) on weather and climate forecasting; (2024) AI-IoT integration for geo-disaster management: Case studies related to earthquakes, Chamola et al. (2021) on disaster mana