Flood susceptibility zonation is pivotal in disaster risk reduction, particularly in flood-prone regions. This chapter introduces a hybrid novel ensemble approach for GIS-based flood susceptibility zonation, incorporating a range of prediction indices to enhance the precision and robustness of flood hazard mapping. The method combines traditional hydrological and geomorphological indices, for example, slope, aspect, Topographic Wetness Index (TWI), profile curvature, plan curvature, convergence index, river density, land use, and Normalized Difference Vegetation Index (NDVI), and can be scrutinized applying frequency ratio and machine learning algorithm, such as logistic regression. By integrating these diverse models, the ensemble approach captures complex interactions between environmental factors, improving the accuracy of flood susceptibility assessments. This hybrid model also addresses the limitations of single-model approaches by leveraging the strengths of each predictive technique, leading to reduced uncertainties and more reliable flood risk maps. Case studies from the Wonoboyo watershed demonstrate the effectiveness of this approach in generating high-resolution zonation maps, proving it a recommendable apparatus for flood risk management, infrastructure planning, and disaster preparedness. This chapter contributes to the growing knowledge of ensemble modeling in geospatial flood prediction and offers insights for future research and applications.