An integrated framework combining Finite Element Analysis (FEA) and Artificial Neural Networks (ANN) is presented to enhance the prediction and design of bioprinted scaffolds. By leveraging the strengths of data-driven learning and physics-based simulations, the hybrid approach (ANN + FEA) achieves superior predictive accuracy and generalization compared to standalone approaches. Validation against experimental results demonstrates that a single ANN model yields a relative error of 5.17% when predicting scaffold Young’s modulus. Incorporating FEA simulation based on ANN-predicted geometry and material properties reduces the relative error to 4.72%, representing an 8.6% improvement. The framework also enables the accurate simulation for unseen combinations of printing parameters located far from the experimental data manifold, reducing prediction errors from 14.2% (ANN-only) to 5.7% (hybrid). By integrating predictive modeling, simulation, and data augmentation, this approach offers an efficient pathway for optimizing scaffold designs and accelerating the development of biomaterials with tailored mechanical performance.