 This paper presents a new approach to predicting the mechanical properties of 3D-printed concrete. The authors developed for machine learning models Gaussian process regression, decision tree regression, support vector machine, and XG boost regression to predict the flexural and tensile strengths of 3D-printed concrete. The models were tested on six different mixed proportions from the dataset and found to be accurate. The results showed that the SVM model outperformed the other models in terms of R2, RMSE, MAE, and MSE. Additionally, the authors noted that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes their work a novel innovation in the field. This article was authored by Amar Ali, Raja the Lavarrias, Umair Jaleel Malik and others.