Heat stress is harmful to the health and productivity of dairy cows. While several models have been developed to assess heat stress conditions for dairy cows, many of these models assume a particular relationship, chosen by the researcher, between environmental conditions and their physiological responses to heat stress. These assumptions may not accurately represent the underlying effects. This study uses machine learning algorithms to evaluate how environmental heat stressors (air temperature, AT; relative humidity, RH; solar radiation, SR; and wind speed, U) influence physiological responses (respiration rate, RR; skin temperature, ST; and vaginal temperature, VT) of dairy cows. The advantage of this approach is that many machine learning algorithms automatically consider nonlinearity in data, which removes subjectivity from researchers choosing the relationship between the predictor and response variables. Four algorithms were considered in this study: penalized linear regression, random forests, gradient boosted machines, and neural networks. Nonlinear machine learning algorithms (random forests and neural networks) were consistently the most accurate in predicting the three physiological responses. The root mean squared error, RMSE, for RR was 9.695 respirations per minute, which was obtained with a random forest model; RMSE for ST was 0.334 °C, which was obtained with a random forest model; and RMSE for VT was 0.434 °C, which was obtained with a neural network model. Air temperature had the highest ranking for effect on RR, ST, and VT. Wind speed and its interaction terms displayed much lower effects as environmental heat stressors. Ranking of environmental heat stressors could help farmers make evidence-based interventions before anticipated stressful environmental conditions occur. Early intervention could improve animal health, which would increase production and reduce the costs associated with treating heat stressed livestock.