Prediction of process route from materials with a desired set of property is one of the fundamental issues in the perspective of the materials design aspect. The parameter space is often too large to be bound since there are too many possibilities. However, in the areas with limited theoretical access, artificial learning techniques can be attempted by using the available data of the candidate materials. In this study, a computational method has been proposed to predict the different process routes of steel constituted taking composition and desired mechanical properties as inputs. First of all, historical data of the actual rolling process was collected, cleaned, and integrated. Further, the dataset is divided into four different classes based on the rolling process data. Then, to find out the essential characteristics of variables, feature correlation among various features has been calculated. A state-of-art machine learning prediction methods such as logistic regression, K- nearest neighbor, Support vector machine, and the random forest are studied to implement the prediction model. In order to avoid the overfitting of the model, k fold cross-validation is applied to the model, and achieve a realistic prediction result with an accuracy of 97%. The F1-score of the classification model is 0.86, and the kappa score is 0.95, which comply that the model has excellent learning and speculation ability and the precise forecast of steel process routes based on the given input parameters.