Limestone-calcined clay cement commonly known as LC3 is a promising material to tackle the CO2 emissions produced by using the Ordinary Portland Cement (OPC) and can provide a sustainable solution for the construction industry in an ecological and economical way. LC3 system is a combination of Clinker—50%, Clay—31%, Limestone—15%, Gypsum—4%, and other admixtures. This paper is focused on developing a machine learning model for predicting the compressive strength of concrete with LC3. The present study deals with ML that has algorithms of various regression methods such as multiple linear regression, polynomial regression, and backward elimination. The accuracy of the models is predicted based on various parameters of compressive strength. The accepted range can be anywhere from 70 to 90%. In the experimental method, the concrete is cured for around 28 days and then its strength is tested experimentally. But in the process of machine learning, our model gives the strength of concrete instantly based on the prediction of previous datasets. It also helps us to find how much of a particular attribute needs to be added in order to get a specific compressive strength.