<p>The complex, nonlinear, and random interactions between diffraction parameters and the phase composition of altered concrete systems are often not adequately represented by standard modelling methods. The objective of this research is to employ data from X-Ray Diffraction (XRD) to test the applicability of advanced machine learning methods to phase identification and classification in concrete modified with magnesium chloride and ground granulated blast furnace slag (GGBS). After preparing, curing, and testing various mixes of concrete using different proportions of GGBS and magnesium chloride to measure their compressive strength, the most suitable combination was selected for further microstructural analysis. The two models trained were Vision Transformer (ViT) and Extreme Gradient Boosting (XGBoost). Applied and tested on the 2θ intensity profiles obtained in XRD data. In XGBoost, the original XRD dataset was used as input for the model. As an additional improvement to the dataset, a synthetic dataset is available. It was synthesised with the help of Generative Adversarial Networks (GANs). Both datasets were similar in the measurement of the Mean square error MSE and R² values. Additionally, the performance of the vision transformer is also superior to that of the Convolutional Neural Network (CNN). The declassification of XRD images of conventional concrete and magnesium chloride-modified concrete was then verified using a confusion matrix. The findings reveal that XGBoost and vision can provide comparable results. A transformer can be a helpful method for accurately interpreting XRD data, providing new insights. It allows identifying and defining phases more precisely in cement-based materials, which is also in terms of classification.</p>