Geopolymer concrete is an innovative and sustainable material for civil infrastructure, utilizing an alkaline solution and fly ash as binders instead of the cement used in conventional concrete. Among its mechanical properties, the critical stress intensity factor is particularly important, as it represents the material's crack resistance under various loading conditions and governs the crack propagation process. However, most studies on the critical stress intensity factor (CSIF) of geopolymer concrete rely heavily on experimental approaches, which require significant time and resources. To address this challenge, this study aims to develop accurate machine learning models to predict the CSIF of geopolymer concrete. A total of 190 experimental test results were collected from literature studies, encompassing fourteen input factors and one output factor. Based on this dataset, two machine learning models were developed: the decision tree model (DTM) and the support vector machine model (SVMM). The prediction results demonstrated strong correlations between the predicted and experimental test data in both the train and test phases, with R values exceeding 0.93. Furthermore, the reliability of the machine learning models was confirmed, with RMSE values less than 15% of the average experimental test data. Sensitivity analysis revealed that fiber volume fraction was the most influential factor affecting the critical stress intensity factor, contributing between 25 and 40%. Conversely, curing time was identified as the least influential factor, with a contribution of approximately 1.3%. The results of this study contributed to the development of accurate models for predicting the CSIF of geopolymer concrete and helped identify the most influential factor affecting it.