As a potential alternative to Portland cement, geopolymers are getting wider acceptance in the scientific world. On a laboratory scale, the latter is being experimented repeatedly to extract valuable and valid results. To complement the experimental work and to make use of the data that resulted from previous experiments, statistical and mathematical models are developed. This article aims to anticipate test results, extract statistical relationships from measured properties, and therefore minimize the time and trials needed to run experiments in laboratories. Five independent input parameters are measured that cover the SiO2/K2O ratio, temperature, time, liquid to solid ratio and the total water content. For each set of these input variables, the consistency of geopolymers was obtained. Two statistical models have been developed in this regard, the Decision Tree, which is a heuristic machine learning model, and the Logistic Regression which is a probabilistic model that calculates and estimates the probability for a certain mixture, at different time, temperature, and other independent variables, to reach a certain consistency threshold. Both model results indicate sufficient performance, and the modelers can use such methods to predict the consistency (pumping time) trends of an untested geopolymer mixture. The results of our models are further validated by additional statistical tests, such as the receiver operating characteristic curve.