Heavy rainfall, a rise in inflow, and an increase in water discharge during the 2024 monsoon season resulted in severe flooding across India. The increasing need for an early warning system to provide flood inundation depth and extent with a better lead time demands innovative solutions that combine the power of high-performance computing (HPC) with automated data assimilation. The novelty of this work lies in the parallel, automated, priority-based assimilation and preprocessing of actual and forecasted precipitation and evapotranspiration data for hydrodynamic modeling, leveraging high-performance computing. The hydrodynamic model then automatically utilizes the processed data to simulate flood propagation over discretized triangular mesh elements by solving Saint-Venant equations using a finite volume solver. High-performance computing (HPC) systems with a peak performance of 3.1 PFLOPS are employed to enable automated and parallel processing of vast datasets, significantly reducing the time required for data assimilation and simulation over an area of approximately 1.4 million sq. km. The system automates and optimizes data assimilation, preprocessing, and visualization, emphasizing a seamless workflow to enhance lead times, which are critical for advanced flood prediction. It also addresses manpower constraints, simplifies domain expertise requirements, minimizes data handling errors, and reduces time consumption. Remote sensing plays a pivotal role in this study by providing timely and accurate data, which are necessary for the early detection of potential flooding events. Satellite-based precipitation and evapotranspiration data, alongside other remote sensing technologies, are integrated into the automated data assimilation system. These data sources are crucial for accurately estimating water level changes across vast regions, ensuring that the hydrodynamic model receives up-to-date input for flood propagation simulations. By using remote sensing, we enhance the precision and reliability of the flood predictions, allowing for better forecasting of inundation depth and extent over larger river basins. Furthermore, remote sensing aids in assigning the initial conditions for the model.