The wake effect caused by a wind turbine can reduce the wind speed and add turbulence to the wind, thus impacting the power generation efficiency. To effectively enhance the generated power in wind farms, we propose an optimal layout model that is combined with artificial intelligence optimizing algorithms. First, the adaptive genetic algorithm (AGA) is used to optimize the collected wind speed and direction distribution data as the optimization basis. Second, the extreme learning machine (ELM) based on Monte Carlo simulation is used to establish a guidance for determining the turbine relocation from the optimization basis. Simultaneously, the dung beetle optimization (DBO) algorithm is developed to improve the performance of ELM for achieving the optimal solution. The proposed model was tested at six different wind farms and under three different wind condition distribution settings. The simulation results verify that the model is superior to existing algorithms in reducing the wake-effect impact as well as optimizing the wind farm layout.