This paper presents the forecast of monthly and daily solar radiation in the Colombian Caribbean region using time series analysis. Three models are implemented, the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) for the monthly forecast and two machine learning algorithms, support vector machine (SVM) the regression tree (RT) for the daily forecast. Performance in forecasting solar radiation is compared, with and without climatic variables. Data, including historical solar radiation and climatic variables, were collected by the Institute of Hydrology, Meteorology, and Environmental Studies in Colombia (IDEAM). Results indicate that while the SARIMA model provides acceptable forecasting, and machine learning models demonstrate better performance, which can improve enhancing decision-making in local energy planning.