In road projects it is important to obtain a correct value of the mechanical properties of the soils since these come to have a great influence on the pavement designs. In reference to this, it is known that conducting tests by traditional methods implies a high cost, time, and laboratory availability, in this context, using predictive models takes significance and importance to predict those values. The objective of the research was to predict mechanical properties of soils using software based on artificial neural network algorithms. In this article a database of 289 values of granulometric tests, consistency limits, maximum dry density, optimum moisture content and CBR was compiled. The methodology corresponds to a quantitative approach, applied type, correlational level, and non-experimental-cross-sectional design. In conclusion, 4 predictive models were obtained with the Neural Tools software, which are: the GRNN model for MDD, with an R2 of 75% and an RMS of 0.09%, GRNN model for OMC, with an R2 of 78% and an RMS of 1.67%, 2-node MLFN model for the CBR 95% MDD, with an R2 of 79% and an RMS of 5.42%, 2-node MLFN model for the CBR100% MDD, with an R2 of 82% and an RMS of 6.93%. In addition, a comparison of values obtained in the soil laboratory vs ANN was made, where the results show a minimum variation of 0.002% in the MDD, 0.06% in the OMC, 0.03% in the CBR, 95% MDD and 0.04% in the CBR100% MDD.