Knowing chemical soil properties might determine crop management and total yield production. Traditional soil properties estimation approaches are time-consuming and require complex lab setups, refraining farmers from promptly taking steps towards optimal soil and plant nutrition. Soil properties estimation from its spectral signal, vis-NIRS, has emerged as a low-cost, non-invasive, non-destructive, and rapid alternative. Using vis-NIRS to estimate properties in different soils can determine the viability of property estimation using spectral reflectance in the range of 400 to 2500 nm. We tested four regression and six classification techniques to estimate soil chemical properties using vis-NIRS spectra from Colombian sugarcane soil samples. Estimated properties were pH, soil organic matter OM, Ca, Na, K, and Mg. We compared performance in soil property prediction, both numerical values and category labels obtained from regressors and classifiers, respectively. We obtained acceptable predictions using regression for pH (R2 =0.81, ρ =0.9), OM (R2 =0.37, ρ =0.63), Ca (R2 =0.55, ρ =0.75), Mg (R2 =0.44, ρ =0.67), confirming the predictive performance reported in the literature. Additionally, we labeled pH (Acc =0.87), OM( Acc=$ 0.73), Ca( Acc =0.72), and Na (Acc = 0.99) with acceptable accuracy. The variability of the soil matrix of the sampling area is an important and valuable limitation for the construction of models. Our results suggest that labeled soil samples and machine learning classifiers might help as a potential tool for supporting decision-making processes in soil and plant nutrition for agriculture.