The binder jetting additive manufacturing (BJAM) process involves multiple parameters such as layer thickness, reference voltage, powder spreading speed, drying speed, and roller speed etc. Traditional experimental parameter optimization faces severe challenges. Recent generative AI (Gen AI) advancements offer transformative potential for manufacturing. This study proposes a novel stacking NeuroSVK model integrated with a Large Language Model (LLM) to optimize printing parameters and simultaneously predict the relative density and dimensional variation of the printed Ti-6Al-4 V green bodies. A printing dataset of 43 experimental sets was evaluated using K-fold cross-validation for comprehensive model training and validation. Predictive accuracy was assessed via Mean Squared Error (MSE) and the coefficient of determination (RPorter et al. (2023)2). The LLM was fine-tuned with 100 prompts to help evaluate the reasonableness of model-optimized parameters and to develop a question-answering agent. Results show the model's recommendations are very close to the experimental optimum with R2 of 0.94, and the agent evaluates the reasonableness of the recommended parameters. Importantly, the density distribution of green bodies printed using the model's parameters is more stable than with experimentally optimized parameters. Finally, the TC4 engine piston printed using the model-optimized parameters was sintered at 1400 °C for 2 h, and a relative density ≥ 97% was reached. The microstructure consisted of α-phase with an average grain size of 28.09 ± 14.23 μm and β-phase at grain boundaries, with a microhardness of 362 HV, tensile strength of 1015 ± 23 MPa, elongation of 10.2 ± 0.6%, carbon content of 0.14 wt%, oxygen content of 0.28 wt%, and dimensional shrinkage of 15.75%, 14.55%, and 18.34% in the X, Y, and Z directions, respectively. © 2026 Elsevier B.V.