The advancement of 3D-printed polyether ether ketone (PEEK) polymers has revolutionized biomedical applications, yet precise control over surface properties remains a critical challenge for optimizing biocompatibility and antimicrobial efficacy. This study introduces a hybrid machine learning framework that synergizes Variational Autoencoder (VAE)-based data augmentation with neural network modeling to predict and enhance key surface characteristics, including hydrophilicity, cell viability, and antibacterial performance. By expanding limited experimental datasets and identifying critical design parameters, this approach enables data-driven optimization of surface modifications, leading to superior biological interactions and antimicrobial resistance. Experimental validation demonstrates that the proposed framework significantly enhances surface topography and chemistry, promoting improved cell adhesion and antibacterial activity. The scalability and adaptability of this methodology position it as a transformative tool for the rational design of next-generation PEEK-based medical implants and devices, accelerating innovation in biomaterial engineering and personalized healthcare solutions.
