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In recent years, there have been more PV panel installations due to the solar PV panels expanding effectiveness as a source of renewable energy. As various renewable sources like, Biofuel, Geothermal, Biomass, wind and solar etc. out this wind and solar both are extremely intermittent, as well this is difficult to maintain system stability while using an excessive amount of renewable energy. A photovoltaic (PV) system may produce solar energy as an alternative energy source. The PV system is dependent on the current climatic circumstances, including the humidity level, the PV system's surface temperature, and the wind speed. Solar power is one of the most useable renewable energies in this world and utilization of this energy for particular application is also challengeable. Solar power forecasting could be utilized to raise the system resilience by providing system control specialists an estimation of future power generation and by supporting the allocation of hydro power plants with in best possible manner. Forecasting of solar power is essential for an electric grid's balancing of supply and demand. Assuming that solar power generation is dependent on weather and unpredictable nature, this is very difficult and complex for the prediction. In this study, a hybrid model that combines statistical and machine learning techniques is proposed for forecasting future solar power generation. Time series models and machine learning can be used to forecast solar PV energy output, which is vital for many participants in the energy sector. In this study, an ensemble of machine learning algorithms was used to increase the suggested model's accuracy. In this review paper it has been surveyed about solar power forecasting using machine learning and also entitled that which machine learning algorithm is best suitable for forecasting solar power. [ABSTRACT FROM AUTHOR]