Bioink optimization traditionally depends on extensive experimental testing across multi-material compositions to evaluate printability, shape fidelity, and biocompatibility. However, predicting bioink properties accurately is challenging due to the complex, non-Newtonian behavior of bioinks, which limits the effectiveness of traditional models, such as the Power Law and Cross models, especially for heterogeneous compositions. In this study, we introduce a machine learning framework utilizing Bayesian optimization (BO) to predict bioink viscosity across diverse compositions, enhancing extrusion-based bioprinting processes. By adopting a data-efficient BO approach, we leverage limited datasets to inform the model, addressing the data scarcity common in bioink research. Additionally, a novel masking technique defines the feasible parameter space based on domain-specific constraints, refining parameter interactions and guiding sampling point selection to balance exploration and exploitation of the parameter space effectively. This iterative approach fosters the creation of a robust surrogate model for viscosity prediction, achieving accuracy with errors of 10% or less, thereby reducing the experimental workload required for bioink formulation. The model’s ability to accurately predict viscosity accelerates the identification of bioink compositions suitable for extrusion, thereby supporting faster tissue engineering advancements. Our AI-guided framework streamlines the optimization process, enabling more efficient bioink formulation and paving the way for significant progress in the field of tissue engineering by minimizing the trial-and-error traditionally associated with bioink development. This approach represents a promising advancement toward predictive modeling in bioprinting applications, accelerating biofabrication innovations.