As one of the significant generators of greenhouse gases, the construction sector is under tremendous pressure due to the rising concern about global climate change and its detrimental effects on communities. Due to the environmental problems connected to the manufacture of cement, geopolymer concrete () has become a viable option for building materials. The results of this research assist in creating machine learning approaches for predicting the qualities of eco‐friendly concrete, which may be used as an alternative to conventional concrete to help lower emissions in the construction sector. In the current work, three hybrid techniques are used to create predictive models that estimate the compressive strength of when ground granulated blast‐furnace slag () is replaced with natural zeolite (), silica fume (), and various concentrations of . For this purpose, three machine learning methods were considered, named , , and hybridized with Fire Hawk Optimization (). The findings show that all approaches have excellent performance in projecting the of , both in terms of the permitted correlation between observed and predicted values. In conclusion, according to the results provided considering statistical indices, error distribution, and Taylor diagram analysis, it could be resulted that the proposed could be recognized as the outperformed model, where other algorithms were also trustable in prediction procedure of of .