This study acknowledges the usefulness of 3D printing in the fabrication of scaffolds for tissue engineering applications. The tuning of printing parameters to produce the desired mechanical qualities for the indicated application is a costly and time-consuming operation. To tackle this difficulty, the study uses Python to develop three machine learning-based approaches: linear regression (LR), random forest (RF), and extreme gradient boosting (XGB models). These models are utilized to find out the yield strength and to make correlation of among three machine learning approaches. This study involves analyzing 10 combinations of diverse lattice structure gathered from scientific research papers, which having parameters of lattice constant, atomic density, young’s modulus, and yield strength. These datasets are then divided into training (say 80%) and test (say 20%) sets. After constructing linear regression, random forest, and XGB models, the research utilizes correlation heat maps and feature importance analyses to elucidate the impact of machine learning on experimental data. Subsequently, the models are employed to predict yield strength in real datasets, allowing for an assessment of their performance. The findings reveal that the linear regression and XGB model outperforms other studied models. The close R2 values for both training and testing suggest that the model does not suffer from overfitting. This study lays the groundwork for potential extensions, could enhance the predictive capabilities of the models for the mechanical properties of scaffolds in tissue engineering applications.