The ultraviolet index is an international standard metric for measuring the strength of the ultraviolet radiation reaching Earth’s surface at a particular time, at a particular place. Major health problems may arise from an overexposure to such radiation, including skin cancer or premature aging, just to name a few. Hence, the goal of this work is to make use of Deep Learning models to forecast the ultraviolet index at a certain area for future timesteps. With the problem framed as a time series one, candidate models are based on Recurrent Neural Networks, a particular class of Artificial Neural Networks that have been shown to produce promising results when handling time series. In particular, candidate models implement Gated Recurrent Unit (GRU) Memory networks, with the models’ input ranging from uni to multi-variate. The used dataset was collected from Open Weather Map API. On the other hand, the models’ output follows the approach to forecast UV index for future timestep. The obtained results strengthen the use of the Gated Recurrent Unit (GRU) network to handle time series problems, with the best candidate model achieving high performance and accuracy for ultraviolet index forecasting.