Mitigating spill pollution in the Nile River is crucial to protecting aquatic life, water quality, and public health. Extensive studies focused on the assessment of water quality and hydrodynamics of the Nile River, but there have been relatively few studies that have applied integrated hydrodynamic and water quality modeling approaches to simulate actual accidents in the Nile Fourth Reach. The goal of this study is to develop advanced computational models to simulate accidental spills in the Nile River and track the resulting impacts on water quality. Hydrodynamic and water quality simulations were performed using Delft3D software for 144 km of the Nile River, Egypt, from El-Menia to Assuit. Once the hydrodynamic and water quality models were calibrated, two phosphate spill scenarios were modeled under maximum and minimum flow conditions. The spatial distribution of the spill plume along the studied river section was visualized every 12 h following the spill occurrence for both scenarios. The results of the research were calibrated and validated against measured field data. In addition, various error and performance indicators were calculated to thoroughly assess the rigor and reliability of the results. The results demonstrated that flow velocity was the main factor influencing the spill plume characteristics and behavior. Initially, advection force plays a significant role after a spill occurs. After that, phosphate becomes mixed and diluted through dispersion. The spill plume took less time to reach downstream areas during the period of maximum flow compared to minimum flow. Additionally, the concentration of phosphate decreased as the water flowed downstream. The spatial distribution of the spill over time can assist water treatment facilities in developing mitigation strategies to address the spill impacts. However, complex Nile River dynamics demand extensive computational power. Therefore, the model was simplified for spill events, using the modeling capabilities to analyze hypothetical spills and contaminant spread in the absence of real data.