ArtificialRegression techniques intelligence (AI) and Machine learning (ML)Machine Learning (ML) approaches to material science have resulted in substantial advances, notably in comprehending carbon nanotubes (CNTs)Carbon Nanotubes (CNTs). This study uses CASTEP simulations to examine the actual and anticipated responses of five different machine learningMachine Learning (ML) modelsMachine learning models: linear regression (LR)Linear Regression (LR), decision treeDecision Tree (DT) regressionDecision Tree Regression (DTR) (DTR), gradient boosting regression (GBR)Gradient Boosting Regression (GBR), support vector regression (SVR)Support Vector Regression (SVR) and Gaussian Process Regression (GPR)Gaussian Process Regression (GPR). Finally, we employ scatter plots and many comparison studies to evaluate each model in terms of the accuracy of prediction as well as the efficiency and scalability of the task at hand. Much of these studies are important, as it seeks to enhance on the employment efficiency, life span, and versatility of carbon based compositesCarbon based composites in production. The research identified that there is enormous potential for AIArtificial Intelligence (AI) and machine learningMachine Learning (ML) to drive the design and development of carbon-based materials and that fast prototyping is possible with advanced designs. The latter not only fosters creativity, but also pushes advances in material science closer to innovation and the resolution of numerous key sectors including aerospace, automotive, renewable energy, and sustainable structures.