Drought events represent one of the most devastating climate-related risks to agricultural productivity in developing regions, disproportionately affecting smallholder farmers who lack access to conventional risk transfer mechanisms. Traditional indemnity-based insurance schemes have proven economically unfeasible for low-income farmers due to high transaction costs, moral hazard concerns, and expensive loss verification requirements. This review examines the transformative potential of satellite-driven micro-insurance models that leverage remote sensing technologies to provide affordable, transparent, and scalable agricultural risk protection in drought-prone regions. By integrating multi-spectral and multi-temporal satellite observations with parametric index-based insurance frameworks, these innovative models enable objective drought monitoring, automated yield estimation, and trigger-based payout mechanisms without requiring costly field assessments. Key satellite-derived indices including Normalized Difference Vegetation Index, Enhanced Vegetation Index, Soil Moisture Index, and Evaporative Stress Index serve as quantifiable proxies for crop health and water availability, forming the foundation for algorithmic risk assessment and payout determination. This article synthesizes current approaches to satellite data acquisition, drought index modeling, micro-insurance product design, and real-world implementation strategies across vulnerable agricultural landscapes. Evidence from pilot programs demonstrates significant potential for enhancing farmer resilience, though challenges related to spatial resolution, validation accuracy, farmer education, and enabling policy frameworks require continued attention to achieve widespread adoption and long-term sustainability.