Machine learning algorithms are suitable in the management of renewable energy sources in smart grids. One of the most significant challenges in the integration of renewable energy sources into the grid is the intermittency of generation, which makes it difficult to forecast and manage the energy supply. The variability and unpredictability of renewable energy sources can lead to imbalances in energy supply and demand, causing grid instability and affecting energy quality. This can result in power outages, voltage fluctuations, and other disruptions that can affect the reliability of the grid. XGBoost algorithm is suitable for finding the stability of smart grid. It is highly accurate and efficient machine learning algorithm that is well-suited for predicting and analyzing energy generation and consumption patterns in a smart grid. By accurately forecasting energy generation, grid operators can better balance energy supply and demand, reducing the likelihood of grid instability and power outages.