Solar global irradiation is barely recorded in remote areas around the world. The lack of access to an electricity grid in these areas presents an enormous opportunity for electrification through renewable energy sources and, specifically, with photovoltaic energy where great solar resources are available. Traditionally, solar resource estimation was performed using parametric-empirical models based on the relationship between solar irradiation and other atmospheric and commonly measured variables, such as temperatures, rainfall, sunshine duration, etc., achieving a relatively high level of certainty. The significant improvement in soft-computing techniques, applied extensively in many research fields, has led to improvements in solar global irradiation modeling. This study conducts a comparative assessment of four different soft-computing techniques (artificial neural networks, support vector regression, M5P regression trees, and extreme learning machines). The results were also compared with two well-known parametric models [Liu and Scot, Agric. For. Meteorol. 106(1), 41–59 (2001) and Antonanzas-Torres et al., Renewable Energy 60, 604–614 (2013b)]. A striking mean absolute error of 1.74 MJ/m2 day was achieved with support vector regression (around 10% lower than with classic parametric models). Furthermore, the annual sums of estimated solar irradiation with this technique were within the intrinsic tolerance of pyranometers (5%). This methodology is performed in free environment R software and released at www.github.com/EDMANSOLAR/remote for future replications of the study in different areas.