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Prior knowledge of solar radiation potential is a necessity for the decision making and the establishment of solar projects. Current prediction methods are site-dependent and their performance outside the area of application is questionable. Therefore, a solar radiation zoning method using a combination of supervised and unsupervised machine learning techniques and satellite gridded data is proposed. The method uses Agglomerative Hierarchical Clustering (AHC) following the ward’s method based on the monthly mean solar insolation on a horizontal surface (H), and Support Vector Machine Classifier (SVM-C) to assist AHC to effectively classify all the areas based on solar radiation data plus temperature, humidity, wind speed and precipitation. Four solar radiation zones have been established. The zones are homogeneous in space and have distinctive solar and meteorological characteristics. The t-distributed Stochastic Neighbor Embedding (t-SNE) is applied to the obtained zones to distinguish trends within solar radiation zones based on temperature, humidity and wind speed, and a total of 8 sub-zones with distinctive meteorological characteristics have been identified. The resulting solar radiation zones and sub-zones cover all Morocco can help for preliminary assessment and decision making, especially for areas with no solar radiation records.