<p>This research introduces an innovative approach to flood vulnerability reduction by integrating the Particle Swarm Optimization (PSO) algorithm with the Random Forest (RF) model to optimize hyperparameters through a parallel and simultaneous search. This methodology aims to accurately identify flood-prone areas. The study also evaluates the performance of the proposed model against other machine learning models, such as the Alternative Decision Tree (ADTree) and Multilayer Perceptron (MLP). Two datasets were utilized for model analysis: ground-based data, including rainfall, proximity to rivers, and roads, and remote sensing data, including elevation, slope, and land use. The Ottawa-Gatineau region in Canada was chosen for modeling. When both ground and remote sensing data were combined, the RF-PSO model achieved a Kappa coefficient of 0.74, outperforming the ADTree (0.70) and MLP (0.69) models. The study further explored the use of remote sensing data alone, with the RF-PSO model yielding a Kappa coefficient of 0.68, suggesting that even without ground-based data, remote sensing alone can produce reliable results. Notably, when high-resolution remote sensing data was applied, the Kappa coefficient increased to 0.80, demonstrating that improved spatial resolution reduces the dependence on ground-based data, thus enhancing model accuracy. This research highlights the potential of using high-resolution satellite data for flood risk assessment, offering significant insights into crisis management and flood vulnerability reduction.</p>