This study explores, for the first time, the innovative application of machine learning (ML) techniques to predict the compressive strength of biobased concretes. To address these issues, a dataset was compiled from numerous previous studies, encompassing more than 200 different formulations of biobased concretes and 20 variables representing components properties and formulation details. A Decision Tree (DT) model was developed as part of the machine learning approach to analyze the dataset and predict the compressive strength of experimentally tested biobased concrete. This model operates by splitting the data into hierarchical decision nodes based on the most significant variables, creating relationships between input features and output predictions. To maintain computational simplicity, the Decision Tree model's maximum depth was restricted to 6. Results indicated that for the test set, the DT model achieved an R_Squared value of 0.72, a Mean Absolute Error (MAE) of 0.49 MPa, and a Mean Squared Error (MSE) of 0.52 MPa. While its performance still less accurate than more advanced models like the Artificial Neural Network (ANN), the Decision Tree model developed in this study provided valuable insights into the dataset's structure and the relative importance of different material properties; this was clearly demonstrated when testing the model on biobased concretes with a lower compressive strength. It is worthwhile noting that for compressive strength higher than 2 MPa, the DT proposed model unveiled lower performance, indicating the existence of overfitting. In sum, this research offers a fresh perspective by leveraging machine learning to address the complexities of biobased concrete, providing first step towards modeling the behavior of these complex materials using Artificial Intelligence technics.