Flow Duration Curve (FDC) is an essential graphical tool for illustrating the variability of observed historical streamflow. Achieving an advanced understanding of the physical characteristics governing FDCs is crucial for enhancing predictions of FDCs in ungauged basins. However, this remains challenging due to the complex processes that control streamflow components and their interactions. To address this, a novel framework that integrates regionalization and process-based methods is proposed for predicting FDCs in ungauged basins. This framework implements a hydrological similarity-based regionalization method to estimate hydrological model parameters in ungauged basins, enhancing streamflow prediction reliability. It categorizes streamflow into four distinct components based on delayed flow separation: Short-delay, intermediate-delay, long-delay, and baseline-delay. These components are synthesized to construct the FDC, with their interdependencies modeled using a Vine copula structure. A Bayesian-based estimation technique for the copula function parameters is developed to further improve the precision of the predicted FDC. Applied to nine selected MOPEX ungauged basins, the framework demonstrated superior accuracy, especially for low to middle streamflow phases. Moreover, the framework exhibited superior simulation accuracy during the validation period, highlighting its substantial potential for future-oriented water resource management and planning strategies.