Snow and glacier melt (SGM) estimation plays an important role in water resources management. Although melting process can be modelled by energy balance methods, such studies require detailed data, which is rarely available. Hence, new and simpler approaches are needed for SGM estimations. The present study aims at developing an artificial neural networks (ANN) based technique for estimating the energy available for melt (EAM) and SGM rates using available and easy to obtain data such as temperature, short-wave radiation and relative humidity. Several ANN and multiple linear regression models (MLR) were developed to represent the energy fluxes and estimate the EAM. The models were trained using measured data from the Zongo glacier located in the outer tropics and validated against measured data from the Antizana glacier located in the inner tropics. It was found that ANN models provide a better generalisation when applied to other data sets. The performance of the models was improved by including Antizana data into the training set, as it was proved to provide better results than other techniques like the use of a prior logarithmic transformation. The final model was validated against measured data from the Alpine glaciers Argentière and Saint-Sorlin. Then, the models were applied for the estimation of SGM at Condoriri glacier. The estimated SGM was compared with SGM estimated by an enhanced temperature method and proved to have the same behaviour considering temperature sensibility. Moreover, the ANN models have the advantage of direct application, while the temperature method requires calibration of empirical coefficients.