The intersection of 3D printing technology and biomedical applications has opened promising avenues for developing personalized medical devices and anatomical models. However, achieving optimal print parameters for complex biomedical structures remains a significant challenge. This study introduces an optimization framework to determine 3D printing parameters tailored to biomedical applications, with a specific focus on the anti-trichiral honeycomb structure. A systematic parametric study and experimental characterization of this structure were conducted to generate a training dataset for the artificial neural network (ANN). The trained ANN then enables the replacement of experimental trials by providing rapid predictions of Young’s modulus for the printed structure. Additionally, an ANOVA analysis was conducted to investigate parameter interactions, with results demonstrating high accuracy and reliability of the predictions. Our work contributes to advancing 3D printing in biomedicine, highlighting the potential of neural networks to accelerate the development of personalized biomedical solutions.