AbstractThe Navier-Stokes Equations (NSE) are central to fluid dynamics, defining the dynamics offluid flow. Nevertheless, their solution, especially for turbulent flows, is still computationallyexpensive. Conventional numerical schemes such as the Finite Volume Method (FVM) arehighly accurate but come at the cost of heavy computational loads. Conversely, deep-learningbased methods like the Fourier Neural Operator (FNO) offer computational efficiency but lackaccuracy and stability in intricate flow conditions. This work presents a hybrid numericalscheme that combines FVM and FNO to achieve a balance between computational efficiencyand accuracy. The hybrid method utilizes FVM for high-fidelity discretization and FNO forfast solution approximation, with an adaptive correction process to maintain numericalstability. The findings prove that the Hybrid FVM-FNO approach reduces computation timeconsiderably with accuracy comparable to traditional solvers. Comparative results indicate thatthis hybrid approach achieves a 3× speedup compared to FVM with very high accuracy and isthus capable of real-time fluid simulation. This method has far-reaching implications incomputational fluid dynamics (CFD) simulations, such as aerospace, weather forecasting, andbiomedical flow studies