Limestone Calcined Clay Cement (LC3) is recognized as a viable alternative to traditional cement blends, thereby mitigating the environmental impacts associated with the cement industry due to its sustainable characteristics. Calcined clay cements possess the capability to diminish the carbon footprint of the cement producing industry. Nevertheless, precisely forecasting the engineering characteristics of this low carbon cement is difficult due to the multitude of aspects associated with its formulation and hardening processes. This work utilized a machine learning approach to estimate the impact of material composition and hardening conditions on the compressive strength of calcined clay cement. Four supervised machine learning methods are utilized, incorporating nine input features such as Water to binder (W/B) ratio, cement content, clay content (CC), limestone (LS), kaolinite content, calcination temperature, SiO2, Al2O3, and CaO. The specific attribute is the compressive strength. The anticipated data is subsequently validated by evaluation metrics including coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Comparatively, machine learning ensemble algorithms like random forest (RF) yielded highest R2 value of 0.95 and least RMSE value of 6.51 on an average.