In this study, 48 polylactic acid (PLA) samples were produced via 3D printing, incorporating four infill geometries (gyroid, lattice, honeycomb, and linear), four infill rates (15%–60%), and three printing directions ( x , y , z ). Tensile testing revealed anisotropic mechanical behavior, with the x ‐direction consistently outperforming y ‐ and z ‐directions due to layer adhesion dynamics. A machine learning framework leveraging copula‐based data augmentation was developed to predict tensile strength at untested infill rates. The framework employed least squares regression, support vector machines (SVM), Gaussian process regression (GPR), and artificial neural networks (ANNs), augmented with 20,000 synthetic data points to enhance model robustness. Results demonstrated that gyroid geometry in the x ‐direction achieved the highest tensile strength (53.4 MPa at 60% infill), while Lattice patterns underperformed. Data augmentation improved prediction accuracy across all models, with SVM achieving the lowest RMSE (1.53 MPa) and R ² values exceeding 0.87. This study highlights the critical interplay of infill parameters, directional anisotropy, and machine learning in optimizing 3D‐printed PLA components for industrial applications, offering a data‐driven pathway to reduce experimental costs and accelerate material design.