The yield strength property is important to consider while designing high entropy alloys (HEAs). In order to obtain the desired yield strength of HEAs through the experimental method it is a difficult, expensive, and time-consuming process due to the broad composition space available. The room temperature yield strength property of nitrogen-doped (CoCrFeMnNi)100-x-Nx HEAs at preferred thermomechanical conditions was predicted using the machine learning (ML) technique based on the linear regression model in the present investigation. The yield strength prediction result of 2% nitrogen-doped CoCrFeMnNi HEA subsequently cold-rolled 91 (%) and annealed at 850 °C temperature consisting of 563.6 MPa is consistent with the experimental value of 556 MPa. It implies that the yield strength predictions of (CoCrFeMnNi)100-x-Nx HEAs are accurate. As a result, selecting suitable models and material parameters to design a wide range of materials with superior properties attributed to various compositions of HEAs through ML technology could be a potential approach.