The geographic location of Indonesia which climates almost entirely tropical provides exclusive potential for solar energy all through the year. This paper performs identification and prediction of solar irradiance in Eastern area of Indonesia. Modeling and estimation approach is carried out by using Artificial Neural Network (ANN) algorithm. Datasets for training and testing are highly correlated parameters from NASA climatological database
for 20 years of historical data. The results of training and testing procedures in ANN show high accuracy of solar modelling and prediction. The research produces spatial mapping of solar irradiance intensity for the monthly average solar irradiance of 174 distritcs in Eastern Indonesia. The knowledge and information about solar resources availability and its forecasting are essentially useful to learn about the potential of solar energy for power generation. It is beneficial at the early stage of renewable energy planning to implement solar energy power generation, especially for the technology and site selection of solar power generation system.