Wind energy is one of the sources which is still in development in Brazil, however, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, prediction of wind speed with high accuracy is a challenging task. The contribution of this study proposes a framework that combines Singular Spectrum Analysis (SSA) and Variational Mode Decomposition (VMD) based on Machine Learning models to forecast the wind speed of a turbine in a wind farm at Parazinho city, Brazil, using a multi-step ahead forecasting strategy (10, 30, and 60 minutes ahead). The forecasting models of the wind speed time series are k-Nearest Neighbor and Support Vector Regression. The performance of the proposed forecasting models were evaluated by using mean absolute percentage error and root mean square error criteria. The VMD-SSA models outperform the SSA, VMD, and single models in all evaluated forecasting horizons, with a performance improvement that ranges within 0.20%–55.78%. Indeed, VMD-SSA is an efficient and accurate model for wind speed forecasting.