Wind energy is one of the cleanest sources of power, offering various advantages such as low emission of carbon dioxide and reducing reliance on non-renewable energy. On the other hand, wind energy integration into the electrical grid is not easy since its generation varies. For efficient and stable operation of the grid, accurate wind power generation forecasts are essential. This study thus envisions an ensemble model through a combination of two techniques: Random Forest and Linear Regression to forecast an improved error value because each of them is well proficient in particular techniques. Thus, it includes the time and environmental variability through relevant feature engineering; training historical data for improvement of prediction errors are subjected to calculation. It made performance measurements with metrics Mean Squared Error, Mean Absolute Error, and R-squared(R2), among others. The ensemble model achieved an MAE of 8.86 × 10−6, an MSE of 3.86 × 10−10, and an R2 value of 1, indicating superior performance compared to individual models. These results demonstrate the ensemble model’s potential to enhance forecasting accuracy, ensuring more reliable integration of renewable energy into the grid.