Intention for this article is to create bidirectional predictive models for estimation of mechanical properties that is hardness and ultimate tensile strength and input process specifications such as current, voltage, speed, and cooling at wire additive arc manufacturing (WAAM) of low carbon steel using machine learning (ML) techniques such as artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and support vector machines (SVM) based on input–output training data. The developed models have been tested using a separate set of test data and compared in terms of mean absolute mean deviations (MAPD) in predictions for both the outputs and inputs. ANN-based models gave best predictions among the ML models developed for both forward and inverse modeling in WAAM of low carbon steel. The prediction accuracies of the developed ML-based models for both forward and inverse cases were found satisfactory.