This project aims to analyze and predict the energy production of a solar power plant using multiple machine learning algorithms. The main objective is to compare the performance of various models to identify the one that offers the most accurate and reliable predictions. The data used for this study includes meteorological variables such as distance-to-solar-noon, temperature, wind direction and speed, sky cover, visibility, humidity, average wind speed over a period, and average pressure over a period, as well as the energy produced by the solar power plant. Each model was evaluated using regression metrics such as Mean Squared Error (MSE) and the coefficient of determination (R2), as well as classification metrics like accuracy, precision, recall, and F1 score. The results of these evaluations allow for a comprehensive comparison of the performance of different algorithms. The performance of the models was visualized using bar charts, facilitating a clear and concise comparison of the various models based on the mentioned metrics.