Although visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) is an emerging, rapid, non-destructive, and cost effective technology to predict a host of soil biological parameters, the traditional chemometric partial least squares regression (PLS) model often poses challenges during sensor development. In an effort to identify alternatives to the PLS model, three multivariate machine learning algorithms [PLS, penalized spline regression (PSR), and random forest (RF) regression]in conjunction with two spectral preprocessing methods [Savitzky–Golay first derivative and absorbance (ABS)] were compared with respect to 12 soil biological parameters of 123 soil samples. The RF model with ABS spectra successfully predicted all biological parameters with residual prediction deviation (RPD) ranging from 2.60 to 3.60 and outperformed PSR and PLS models. The best PSR model was obtained for total bacteria with an RPD of 2.70 and an r2 of 0.86 and among other variables, only Gram positive bacteria (RPD=2.63, r2=0.85), Gram negative bacteria (RPD=2.58, r2=0.85), and SOM (RPD=2.67, r2=0.86) were satisfactorily predicted, exhibiting r2>0.80 and RPD>2.5. Conversely, all variables except SOM (RPD=2.07) were poorly predicted by PLS models which had an RPD<2. Furthermore, linear discriminant analysis qualitatively clustered soils with different levels of microbial parameters. Summarily, the RF model with ABS spectra showed great promise in characterizing soil microbial communities with potential for such analysis in-situ.