Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large-scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation-wide flood susceptibility mapping, this modeling approach considers ten geo-environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo-environmental input factors. In general, accurate nation-wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well-performing and cost-effective for the case of Sweden, calling for further application and testing in other world regions.