Viscosity is a critical transport property in amine-based CO2 capture systems, as it directly influences solvent circulation, pumping energy consumption, and gas-liquid mass transfer rates within absorption-desorption columns. Despite its importance, viscosity determination in traditional research and industrial practice largely depends on experimental measurements, which are not only labor-intensive and time-consuming but also impractical for evaluating the rapidly expanding library of candidate absorbents. To address this methodological bottleneck, this study establishes a data-driven prediction framework for accurately estimating the viscosity of monoamine aqueous solutions using machine learning techniques. Molecular descriptors are systematically extracted from SMILES representations via RDKit and subjected to feature engineering to eliminate redundancy and enhance model generalizability. Two ensemble-learning algorithms, random forest (RF) and gradient boosting decision trees (GBDT), are developed and optimized using Bayesian hyperparameter tuning. Benchmarking on independent test datasets reveals that the RF and GBDT models achieve coefficient of determination (R2) of 0.9939 and 0.9986, respectively, with GBDT exhibiting higher robustness toward extrapolative predictions under varying temperature and concentration conditions. To elucidate model decision mechanisms, SHAP analysis is employed, revealing that viscosity increases monotonically with amine mass fraction while decreasing with temperature, aligning with thermodynamic expectations. From the perspective of molecular structure, molecular weight (MolWt) makes a positive contribution to overall viscosity, while the electronic state descriptor MinAbsEStateIndex makes a negative contribution to viscosity, indicating that differences in electron distribution can regulate intermolecular interactions and affect viscosity. Overall, this work bridges computational chemistry and process engineering, offering a scalable pathway toward accelerated solvent discovery and optimization for next-generation carbon capture technologies.