Floods are a significant natural hazard, causing severe damage. Understanding how climate change and land use and land cover (LULC) changes influence flood patterns is crucial for developing sustainable management strategies. This research aims to develop flood susceptibility maps considering the impacts of climate change and land use changes, providing insights into risks from urbanization and climate shifts. Three machine learning models-XGBoost, Random Forest (RF), and Support Vector Machine (SVM)-optimized with Particle Swarm Optimization, were applied to the flood-prone Kashkan watershed in Iran. Results showed that distance from the river, digital elevation model, precipitation, and LULC were the most influential factors. The RF model outperformed others in mapping flood-prone areas, with high-risk zones covering 20% (1908 km) of the region, primarily in built-up areas. Land use projections for 2050, using the CA-MARKOV model, estimate built-up areas will expand to 859.3 km. Future precipitation patterns were examined using 8 selected general circulation models under the SSP126 and SSP585 scenarios. Analysis under the SSP585 scenario indicates a 1.9 km rise in moderate flood areas, a 36.26 km increase in high-risk zones, and a 21.94 km decline in very low-risk areas, highlighting expansion of high and moderate flood risk areas.