This study used artificial intelligence (AI) to predict the tensile strength of materials produced through fused deposition modeling (FDM) type three-dimensional (3-D) printing. Samples were produced using a 3-D printer with printing speeds of 40, 70, and 100 mm/s, nozzle temperatures of 190°C and 220°C, raster angles of 0°, 45°, and 90°, layer thicknesses of 0.1 and 0.2 mm, and a constant infill ratio of 100 %. Tensile strength data were obtained through tensile testing. In the conducted experiments, the highest tensile strength was recorded at parameters of 40 mm/s printing speed, 220°C nozzle temperature, 90° writing angle, and 0.1 mm layer thickness, yielding 43.27 MPa, whereas the lowest tensile strength was observed at parameters of 100 mm/s printing speed, 190°C nozzle temperature, 0° writing angle, and 0.2 mm layer thickness, resulting in 7.33 MPa. Tensile strength values obtained from standardized mechanical tests were used to train and evaluate several machine-learning algorithms, including random forest, decision tree, ridge, lasso, and elastic net regressions. Among these, the random forest algorithm yielded the highest prediction accuracy with an R² value of 0.948. The results demonstrate that AI can effectively model the complex relationships between 3-D printing parameters and mechanical performance, offering a powerful tool for optimization in additive manufacturing processes.