<p>Drought, a complex and insidious natural hazard, poses a significant and escalating threat to global water security, agricultural production, ecosystems, and human livelihoods. Unlike sudden-onset disasters, droughts are “creeping phenomena,” slowly developing periods of below-average precipitation and reduced water availability that can persist for months to years, with cascading socio-economic consequences. In an era profoundly shaped by anthropogenic climate change, understanding and predicting drought dynamics are more critical than ever. Climate change exacerbates drought conditions, increasing their frequency, intensity, and duration, though impacts vary across regions. Key drought indices, including the Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI), and the Palmer Drought Severity Index (PDSI), are crucial for assessing drought severity. Advances in remote sensing and multi-variable indices have significantly improved monitoring accuracy, particularly for agricultural droughts. The application of artificial intelligence (AI) and machine learning (ML) has enhanced drought prediction by integrating multiple climate variables, though challenges related to data reliability and model transparency remain. High-resolution datasets, such as daily SPI/SPEI and weekly drought indices, contribute to more precise monitoring and forecasting. Future projections indicate an increase in agricultural and hydrological droughts, highlighting the importance of multi-index assessments and advanced climate models for improved forecasting and resource management. Ongoing developments in drought indices, AI-driven analysis, and high-resolution monitoring are crucial for strengthening drought mitigation efforts, supporting climate adaptation, and ensuring effective water resource planning.</p>