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High strength concrete (HSC) (50–100 MPa) and ultra-high strength concrete (UHSC) (>100 MPa) have been increasingly used in the construction industry due to its inherent performance characteristics. However, these concrete mixes have a higher carbon footprint and it is vital to consider the embodied carbon of the HSC and UHSC due to the massive consumption throughout the world. In this study, embodied carbon analysis, using machine learning algorithms has been carried out to minimize the carbon footprint of concrete without jeopardizing the mechanical properties of the concrete. Machine learning models are developed using experimental results in the literature and used to predict the compressive strength of concrete using the constituent materials. Using the experimental data and machine-learned models for mix designs, embodied carbon emissions were calculated. It is shown that there can be …