<p>Effective flood disaster preparedness and mitigation techniques require flood susceptibility mapping (FSM). However, existing maps do not offer the stakeholders a good understanding and clear rationale for decision-making, as they lack a comprehensive and intuitive spatial representation. To bridge this gap, this research suggests a novel approach that exploits the immersive experience of virtual reality (VR) that engages users in a virtual setting further synchronized with the appropriate machine learning algorithms to interpret and explore the flood risk exposure in the first place. Initially, the research fine-tunes the random forest (RF) model with the invasive weed optimization (IWO) algorithm to enhance the precision of flood susceptibility forecasts. Additionally, the study utilizes VR technology to present flood susceptibility maps and important spatial factors in a setting. The research focused on Kazerun and Kooh Chenar regions in the part of Irans Fars province. In this study, the areas impacted by monsoon floods in 2022 and fourteen critical spatial factors were used as inputs for the modeling process. According to evaluation metrics like root mean square error (RMSE) (training 0.11 and testing 0.21), mean absolute error (MAE) (training 0.042 and testing 0.092), coefficient of determination (<i>R</i>²) (training 0.94 and testing 0.81), and area under the receiver operating characteristic (AUC-ROC) curve (90%), the RF-IWO model produced more accurate flood susceptibility maps than the RF model. The combination of optimal machine learning techniques and VR visualization can convincingly contribute to a tool for analyzing flood risk and zonal planning in future flood risk management.</p>