<p>Flooding is a significant natural hazard intensified by both human activities and environmental factors, posing risks to both ecological integrity and public safety. Effective flood susceptibility mapping (FSM) is essential for risk assessment and mitigation strategies. This study evaluates the performance of eight fuzzy logic operators within the fuzzy analytical hierarchy process (F_AHP) framework for assessing flood susceptibility in Koya, Iraq. By integrating nine environmental factors-including elevation, slope, topographic wetness index (TWI), land use/land cover (LULC), drainage density (DD), distance to streams ( D to S), distance to roads (D to R), precipitation, and sediment transport index (STI), the research identifies areas prone to flooding and offers the first comprehensive comparison of fuzzy operators for FSM in Iraq. Model performance was validated against 32 recorded flood events from 2010 to 2024 using the area under the receiver operating characteristic curve (AUC-ROC) metric in ArcGIS, and flood susceptibility was categorized into five classes: very high, high, moderate, low, and very low. The Gamma, AND, and PRODUCT operators demonstrated superior predictive capabilities, achieving AUC values of 95%, while the OR and SUM operators were less effective, with AUC values of 86% and 70%, respectively. The most accurate models identified high-to-very-high susceptibility zones, comprising about 2.21% of the study area. This study presents the first systematic 8-operator comparison of FSM in Iraq’s semi-arid context, including the most extensive gamma value range (0.70–0.99) tested in Middle Eastern semi-arid areas. These findings enhance methodological insights and support the use of specific fuzzy operators for FSM, understanding their importance in flood risk management in semi-arid regions.</p>