Bioprinting has great potential in tissue engineering and regenerative medicine applications due to the possibility of creating functional tissue replacements for damaged tissues or organs. However, current methods for optimizing printing parameters are time-consuming and heavily rely on operators’ experience. In this study, Pluronic F127 and sodium alginate-Laponite (Alg-Lap) bioinks were used to evaluate the quality of four bioprinted constructs: tubular, angular, crosshatch, and overhang structures. Machine learning (ML) algorithms, including Multilayer Perceptron (MLP), Random Forest (RF), Gaussian Naive Bayes (GNB), Classification and Regression Trees (CART), Support Vector Machines (SVM), and Logistic Regression (LR), were evaluated for classifying constructs into print-quality categories using pre-print parameters and post-print geometry-specific quality measurements. RF performed best for the tubular, crosshatch, and overhang structures, achieving classification accuracies of 91.9%, 95.3%, and 96.0%, respectively, while MLP performed best for the angular structure, achieving 96.3% accuracy. Balanced accuracies for these best-performing models were 85.8%, 91.8%, 86.9%, and 90.8% for the tubular, crosshatch, overhang, and angular structures, respectively. These findings demonstrate the potential of ML-based quality classification to support more reproducible assessment of extrusion-based bioprinting outcomes, advancing tissue engineering and regenerative medicine applications while enhancing efficiency and reproducibility.