Abstract The Additive Manufacturing technology initially developed as a visualization and validation tool. The recent advancement in this technology like Fused Deposition Modeling (FDM), Stereolithography (SLA) and Selective Laser Sintering (SLS) make it rapid manufacturing. However, the manufacturing of ready to use parts using FDM is a challenging task. The processing parameters like infill density, infill pattern, extrusion temperature affect the design, quality, functionality and mechanical properties of the final part vigorously. The present article discusses the effects of material density, infill density and extrusion temperature on tensile strength of Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PETG), and Multi-material test pieces. Multi-material is fabricated by merging 50% of ABS and 50% of PETG layer by layer in FDM 3D Printing. A total of 30 test pieces as per the ASTM D638-(IV) standard was printed having different infill density, extrusion temperature, and material density. In addition to this, Infill density and extrusion temperature are optimized for increasing the tensile strength of FDM fabricated units. Artificial neural network (ANN) and genetic algorithm-artificial neural network (GA-ANN) hybrid tool in MATLAB-16.0 are used for training and optimization purposes. It is observed that GA-ANN has maximized the tensile strength up to 4.54% and it has been validated experimentally.