Wind power forecast has been considered important when it comes to the expansion of renewable power sources in power system since they are variable. This work seeks to analyze the capabilities of a number of classifiers namely Gaussian process regression (GPR), support vector regression (SVR), and ensemble learning models in forecasting wind power output. The focus of the study is placed on hyperparameters tuning using Bayesian optimization (BO) and the inclusion of data lags that can help improve the accuracy of the forecast. The datasets of wind turbines are used to assess the innovation of models. The performance is validated by taking dataset from Kaggle repository. The study’s analysis shows that the GPR model and ensemble learning methods, mainly the dynamic modeling techniques, achieve high accuracy in the prediction process compared to conventional methods with the optimized GPR and ensemble learning models providing the finest results. From this research, it is clear that new ML algorithms have the capability of enhancing the status of wind power forecasts and thus advance in the comprehensive deployment of renewable energy into the grid system. As a result of the current work, future work will aim at improving the model robustness and bringing the applications to real-time forecasting. Still, the results highlight the need for using predictive ML models to tackle the challenges inherent in wind power forecasting.