Accurate yet efficient wake modeling is essential for wind farm layout optimization (WFLO). Wind turbine wakes are disturbed regions of flow behind a wind turbine, characterized by lower mean wind speeds and higher turbulence, which reduce downstream power production and increase structural loading. This study compares an Artificial Neural Network (ANN)-based surrogate trained on Reynolds Averaged Navier-Stokes (RANS) data with two representative engineering wake models based on the TurbOPark and Super-Gaussian formulations. The work includes recalibration of the engineering models, a systematic flow simulation study across varying turbine counts and spacings, and WFLO benchmarks validated against RANS-based Annual Energy Production (AEP). Results show that the ANN surrogate achieves the lowest RMSE and MAPE across all scenarios in flow estimation, albeit at a higher computational cost. In WFLO, the TurbOPark-based model produced the highest RANS-validated AEP layouts, despite having lower predictive accuracy, suggesting that optimization complexity influences outcomes. Blockage modeling increased computational cost without improving accuracy.