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The study aims to investigate the possibility of employing machine learning models in the design of non-involute gears. Such a model would be useful for design calculations of non-standard gears, where there are no available guidelines. The aim is to create a decision-support model accompanying the Finite Element Method (FEM) simulations, from which the data for training was collected. Multiple models for numerical prediction were tested, i.e. linear regression, Support Vector Machine, K-nearest neighbour, neural network, AdaBoost, and random forest. The models were firstly validated with N-fold cross-validation. Further validation was done with new FEM simulations. The results from the simulations and the models were in good agreement. The best-performing ones were random forest and AdaBoost. Based on the validation results, a machine learning constructed model for calculating nominal root stress in …