In recent years, there has been a rapid growth in the utilization of electricity generated from renewable energy sources. Solar energy stands out as a promising solution to the need for a sustainable and easily accessible power source. The integration of intermittent solar PV (photovoltaic) systems into the electricity grid can have a substantial impact on its stability. The implementation of dependable and accurate power forecasting is of utmost importance to enhance grid stability, optimize energy management, and maximize the economic advantages of photovoltaic (PV) power generation. To study the prediction capabilities, three distinct machine learning models, namely Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boost (XGBoost), were selected and subjected to refining and testing. The chosen models are briefly described. The photovoltaic (PV) power is computed by utilizing weather data for a span of one year, and subsequent generation forecasting is conducted based on the computed PV power. The evaluation of the performance of the chosen tools is conducted by assessing metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Normalized Root Mean Squared Error (NRMSE), and R-squared (R2) values. The findings of this study not only contribute to the improvement of the integration of solar energy into the power grid, but also offer useful insights into the selection of machine learning techniques for accurate and efficient forecasting of solar photovoltaic (PV) generation.