In the struggle to achieve environmental management goals, wind energy is essential. On the other side, the effectiveness of the wind turbines is a prerequisite for wind energy to reach its full potential. The purpose of the research is to examine how artificial intelligence (AI) techniques are applied to wind speed. In the context of wind speed forecasting, three network parameter optimization algorithms—AdaGrad, RMSprop, and Adam—are applied and contrasted in this work. The wind speed data used in this article came from the Saudi Arabian Jeddah Al Jazeera database. The four metrics used to evaluate performance are mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R-squared. According to the experiment's findings, the Adam algorithm performs better than the other optimization techniques.