​This study presents a comprehensive data-driven approach to predicting optimal tilt angles of photovoltaic systems using five optimized machine learning models and data from 12,499 global locations obtained from the Photovoltaic Geographical Information System (PVGIS). First, we present an investigation of the prediction accuracy of 40 different feature combinations spanning the latitude, longitude, elevation, temperature, relative humidity, wind speed, global horizontal irradiation, and diffuse horizontal irradiation of each location. Second, we evaluate the impact of four different data resolutions on model performance, including annual data, annual data with annual variance, monthly data, and monthly data with monthly variance applied to the meteorological features. Third, we consider the impact of treating latitude as an absolute value for all cases. We find that breaking down meteorological data into monthly resolution significantly improved prediction accuracy, achieving a root mean square error as low as 1.029° and accuracy as high as 99.27 % using a multilayer perceptron model, while the use of latitude as an absolute value should be evaluated on a case-by-case basis. Validation with in-situ and satellite data confirmed the robustness of our models, with PVGIS data proving effective in predicting tilt angles consistent with both real-world and satellite-based measurements. We emphasize that no single model, feature combination, or data resolution was universally superior in the validation phase. Both minimum and maximum data as well as model complexity achieved acceptable and reliable predictions across different regions and conditions.​