<p>Drought is a pervasive climatic risk in Iran, but its impacts are spatially and temporally heterogeneous, making a granular understanding of regional susceptibility critical for effective mitigation. This study provides a high-resolution spatiotemporal assessment of Iran’s drought susceptibility, from historical analysis to future projection. We first mapped historical susceptibility (1968–2024) by integrating 19 criteria using the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP). To project future scenarios for 2026–2034, we employed and validated two advanced predictive models, the Integrated Process with Cellular Automata (IMP-CA) and Convolutional Long Short-Term Memory (ConvLSTM), trained on the recent historical map series (2000–2024). The resulting susceptibility maps were categorized into four classes: mild drought susceptibility class (DSC₁), moderate drought susceptibility class (DSC₂), severe drought susceptibility class (DSC₃), and very severe drought susceptibility class (DSC₄). The results reveal a fundamental transformation in Iran’s drought landscape. Our projections show that the severe susceptibility class (DSC3) is set to become the dominant national category from 2024 onwards, displacing the historically prevalent moderate class (DSC2). This critical shift is substantiated by Sen’s slope estimator (SSE) trend analysis, which confirmed a significant expansion of DSC3 areas at the expense of shrinking DSC1 and DSC2 zones over both historical (2000–2024) and combined (2000–2034) periods. The ratio of rain-fed to total agricultural land was identified as the most influential criterion. We pinpointed escalating drought hotspots in regions stretching from the northwest to the southwest and across central Iran. This research provides actionable intelligence on an accelerating national risk, offering a proactive tool for policymakers to prioritize water resource management and climate adaptation efforts in Iran’s most vulnerable regions.</p>