The incorporation of biofillers into the polymer matrix as reinforcements enhances the stiffness and tensile strength of bio-nanocomposites. Fused filament fabrication (FFF) is a 3D printing technique and because of its simplicity and affordability it is used in mass scale for product making and prototyping. The expense and performance of components are affected by several printing parameters, complicating the selection of optimal process settings for part quality. This work predicts the mechanical properties of FFF printed filler-reinforced PLA composite specimens using ensemble machine learning methods. Tensile strength, toughness, warpage, and elastic modulus are among the quality parameters that are intrinsically conflicting in the FFF printing process. Thus, an effort is initiated to imitate the cumulative impact of all the quality criteria, resulting in overall quality parameter index (OQPI). TOPSIS integrates various quality parameters to yield a solitary value (OQPI). Additionally, machine learning modeling approaches including Random Forest, Adaptive Gradient Boosting, Extra Tree, and Gradient Boosting are utilized for OQPI modeling through hyperparameter optimization. This study demonstrates the efficacy of the ABR model in predicting quality characteristics, evidenced by MAE of 0.039, RMSE of 0.044, and MAPE of 16.13%. The findings illustrate innovative approach utilizing ensemble machine learning methods to uncover complex relationships within the database, thus laying the framework for enhanced product conception and optimization of mechanical properties.