This study presents an advanced Learning Enthusiasm based teaching–learning algorithm (LebTLBO) for optimization of line width compensation, extrusion speed, filling speed, and layer thickness of the Fused Deposition Modeling (FDM) process. These independent parameters have a notable effect on dimensional accuracy, warp deformation, and manufacturing time which further affects the usability of FDM parts. A comparative analysis of LebTLBO has been performed with conventional algorithms for solving a complex optimization problem. This optimization tool simulates the classroom’s teaching–learning process and demands only the common control parameters i.e. number of generations and population size instead of algorithm-specific control parameters. The findings show that LebTLBO is most efficient to chalk out single parametric settings which would maximize each response. The optimum value of parameters predicted by LebTLBO is 0.15 mm layer thickness, extrusion speed of 21.85 mm/s, and 40 mm/s filling speed for acrylonitrile butadiene styrene thermoplastic material. Confirmatory experiments validate the efficiency of LebTLBO as the maximum response is achieved at predicted parametric settings.