Drought-induced crop failures represent a critical threat to food security and rural livelihoods in vulnerable agricultural regions, where traditional indemnity-based insurance schemes remain economically unviable due to high transaction costs, moral hazard, and limited infrastructural capacity for field-level loss assessment. This review examines the transformative potential of satellite-driven micro-insurance models that leverage remote sensing technologies for objective, scalable, and cost-effective drought risk assessment and automated claim settlement. We synthesize current advances in satellite data acquisition platforms, drought indices derived from multispectral and radar imagery, and algorithmic approaches for crop monitoring and yield estimation. Key micro-insurance frameworks including index-based, parametric, and hybrid models are evaluated with emphasis on their integration with vegetation indices such as NDVI, NDWI, and VCI, alongside machine learning algorithms for predictive analytics. Field implementations across Sub-Saharan Africa, South Asia, and Latin America demonstrate significant improvements in payout accuracy, reduced basis risk, and enhanced farmer adoption rates when satellite data replaces conventional ground surveys. Despite persistent challenges related to spatial resolution, cloud cover interference, and digital literacy barriers, satellite-enabled micro-insurance emerges as a scalable climate adaptation strategy with substantial potential for protecting vulnerable farming communities against intensifying drought risks under global climate change scenarios.