<p>The geopolymer concrete composites have different properties with the materials incorporated as precursors. Thus, knowing their mechanical properties is very important for their application as a building material. The significant property for its utilisation is the Compressive strength (CS). To predict the CS of geopolymer concrete, utilisation of the Machine learning (ML) is essential. This study includes the collection of data from the experimental work and the application of ML techniques to predict the CS of geopolymer concrete containing fly ash and other additives. The machine learning algorithms such as Gradient Descent in Linear Regression, Ridge Regression, Adaboost Regression, Decision tree regression, Random Forest Regression were investigated for the prediction of outcome (CS). Total 256 data points were collected from the works published by the researchers, in which eleven parameters namely fly ash, GGBS, molarity of NaOH, Si/Al ratio, coarse aggregate, fine aggregate, NaOH/Na₂SiO₃ etc. were taken as input to predict the output which was CS parameter. The experimental data is further validated by mean of k-fold cross-validation using R², root mean error (RME), and Root mean square error (RMSE). In addition, statistical checks were incorporated to evaluate the model performance. In comparison, the Random Forest regressor shows high accuracy towards the prediction of outcome as indicated by its high coefficient correlation (R²) value equals to 0.99, while R² value for gradient descent in linear regression, Ridge regression, Adaboost regression and Decision tree regression comes to 0.354, 0.68, 0.853 and 0.976 respectively.</p>