The structural and operational optimization of gas-liquid stirred bioreactors presents both complexity and critical importance for enhancing mass transfer performance. This study proposes a machine learning (ML)-driven approach to identify key features and predict the volumetric mass transfer coefficient (

k

L

a

). Four ML models were adopted and compared for

k

L

a

prediction in Newtonian and non-Newtonian fluids by evaluative indices

with CatBoost and XGBoost emerging as the optimal models

respectively. Specifically

it is demonstrated that Catboost has higher prediction accuracy (AARD = 18.84%) than empirical equations by effectively incorporating multidimensional features (structural

impeller

and operational)

while simultaneously extending applicability to diverse Newtonian fluids. For non-Newtonian fluids

XGBoost outperforms empirical equations by effectively incorporating fluid rheological parameters (consistency coefficient

power-law index)

thereby better capturing shear-thinning behavior. Feature importance analysis further identified rotational speed (for Newtonian fluids) and liquid height (for non-Newtonian fluids) as the key features

while 2D par

tial dependence analysis establishes quantitative optimization ranges. This ML approach provides an efficient predictive tool for gas-liquid stirred bioreactor design and optimization.