Solar energy has the potential to help combat climate change and promote sustainable development. However, the efficiency and reliability of solar energy systems heavily depend on accurately forecasting power output. The aim of this research is to identify which machine learning algorithm from the python scikit-learn library is most effective for predicting AC power output from solar cells. Unlike previous models that poorly handled environmental variability or were too simple for capturing the multifaceted nature of solar cell operations, this research integrates diverse input parameters, such as daily yield, total yield, ambient temperature, module temperature, irradiation, and monthly data. After testing multiple algorithms, the Random Forest Regressor emerged as the most accurate one, with a 0.968 R-squared score. This exceedingly high accuracy measure indicates its proficiency in identifying factors affecting solar energy output and predicting solar energy generation with minimal errors. The predictions generated by this model are valuable for many players in the renewable energy sector. Using such models, grid operators can optimize power distribution, solar companies can improve equipment and tracker placement, and investors can improve financial returns for solar investors. This type of data-based decision making tool is valuable for developers, vendors, and partners in maximizing the potential of renewable energy installations.