All measurements have error that obscures the true value. The error creates uncertainty about the quality of the measured value, which is requiring testing and calibration laboratories to provide estimates of uncertainty with their measurements. Measurement uncertainties include input uncertainty, the propagation of input uncertainty, the output uncertainty and the systematic error uncertainty. Several methods for estimating the uncertainty of measurements have been introduced for different kinds of uncertainty quantification, and two data mining methodologies-Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used to build the unknown propagation model. This paper will discuss the quantification of measurement uncertainty (MU) and the separation of various uncertainty sources to MU and will discuss the advantages and limitations of SVM and ANN for building the propagation model of MU.