In this study, a universal method for estimating monthly solar radiation on tilted surfaces from horizontal measurements was developed and validated, targeting the optimal design and performance of solar energy systems. Using data from the Photovoltaic Geographical Information System (PVGIS) across 740 global locations, three machine learning models—linear regression, random forest, and k-nearest neighbors (KNN)—along with a multilayer perceptron deep learning model, were evaluated. The dataset spanned tilt angles from 0° to 90° at 1° increments and 11,511,773 rows with six input features for predicting irradiance on any tilt angle. The KNN model was selected for further testing analysis due to its superior accuracy, achieving the lowest root mean square error (RMSE) of 1.42 kWh/m² and mean absolute error (MAE) of 0.7 kWh/m² on the validation dataset. Testing was conducted with 16 years of data from five locations not included in the training dataset, obtained from PVGIS and Solcast. Results show that the KNN model consistently outperformed the traditional isotropic model in four out of five cities using PVGIS data and in two using Solcast data, especially excelling in regions with stable climates and consistent irradiance profiles. Moreover, better prediction performance was observed with PVGIS data compared to Solcast data across all five cities, with PVGIS data yielding an RMSE of 3.03 kWh/m² and an MAE of 2.41 kWh/m², while Solcast data exhibited a higher RMSE of 5.22 kWh/m² and MAE of 4.14 kWh/m². Finally, these results advocate for a shift towards data-driven methodologies highlighting their enhanced reliability.