This paper focuses on the modeling of the performance of photovoltaic systems based on advanced techniques. This research leverages real-world data from the Shams Solar Facility at the German University of Technology in Oman to explore the application of Linear, Lasso, Ridge, and Elastic Net Regressions to predict and optimize the performance of photovoltaic systems. A comprehensive dataset of 36,851 observations of environmental and operational conditions forms the basis of the analysis. The research identifies the strengths and limitations of these modeling techniques for an accurate forecast of energy output under various scenarios. The comparative analysis highlights the precision and reliability of each regression method and offers actionable insights into their practical implementation. The findings highlight the importance of more sophisticated modeling approaches in increasing the knowledge of photovoltaic system dynamics and optimizing their performance. This research facilitates the advancement in solar energy systems and provides critical recommendations for the improvement in efficiency and reliability of photovoltaic installations under different geographic and climatic settings.