<p>Conventional modelling methods have been criticised to lack adequate applicability in characterising complex, nonlinear, uncertain associations between diffraction parameters and the phase composition of the modified concrete system. The purpose of this work is to conduct a study in which a framework based on machine learning can identify the phase in the bentonite calcite modified concrete based on X-ray diffraction (XRD) data. Concrete mixtures of different proportions of Ground Granulated Blast Furnace Slag (GGBS), bentonite and calcite were prepared, cured and tested to evaluate their compressive strength and the ideal mixture was chosen upon which further analysis was carried out. Concerning this mix, the 2θ-intensity profiles obtained by the X-ray diffraction data were taken to train and test two kernel-based regression models: Support Vector Regression (SVR) and Gaussian Process Regression (GPR), employing 80:20 ratio as the splitting line in the whole X-ray diffraction data. SVR showed good general capability because it successfully applied to high-dimensional data learning by optimizing on a margin. GPR conversely involved using a probabilistic kernel in order to establish latent nonlinearities and uncertainty on the data. The predictive performance was high in both models, where SVR had R² of 0.978 and 0.977 on training and testing respectively, and GPR had a R² of 0.982 and 0.979 on training and testing respectively. Also, the values of mean squared error confirmed the superiority of GPR. The results confirm the promising nature of machine learning, specifically SVR and GPR, as scalable and fully competent alternatives to established techniques in phase quantification, which are reliable and competent in capturing the microstructure, and there upon offers sensible guidelines in optimizing the microstructure of novel cementitious composites.</p>