Mozambique's coastal regions are prone to flooding due to its location in a semiarid climate zone bordering tropical cyclones and monsoons. A combination of remote sensing imagery and hydrological data was used to identify flood-prone zones. A multinomial logistic regression model was employed to predict community readiness levels based on available resources and infrastructure. The GIS mapping identified a 45% probability of flooding in the coastal regions, with specific areas along the Zambezi River facing higher risks (over 60%). The multinomial logistic regression revealed that communities with better access to early warning systems had preparedness success rates between 20-30%. The GIS mapping and early warning system development have significantly improved community resilience against coastal flooding in Mozambique. Communities should be provided with regular training on flood mitigation strategies, and local authorities need to allocate resources for infrastructure improvements targeted at high-risk areas. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.