As heat generation density in semiconductor devices increases, effective thermal management and measurement become essential, requiring the evaluation of multiple thermophysical properties such as volumetric heat capacity and thermal conductivity. The three omega method offers a simple and precise technique for measuring the thermophysical properties of a wide range of materials by analyzing the frequency response of the third-harmonic voltage and fitting an analytical solution to the results. For accurate measurements of micro- and nanoscale materials, nonlinear fitting–based analysis is indispensable at higher frequencies. Moreover, simultaneous determination of multiple thermophysical properties, including thermal conductivity, heat capacity, and thermal boundary conductance, also requires nonlinear fitting techniques. However, the nonlinear fitting process is unstable due to its dependence on the initial guess and its tendency to converge to a local minimum. In this work, we introduce a machine learning–based prediction method for materials with uniform or nonuniform thermal conductivity in three omega measurements. First, we have developed machine learning models capable of predicting thermal conductivity and volumetric heat capacity. We confirmed the superior stability of the machine learning model in predicting thermophysical properties through comparative simulations with machine learning-based and conventional fitting methods. We further validated the machine learning model using experimental data on water, IPA, and their mixture, confirming that the predictions were in good agreement with the literature and theoretical values. In addition, we developed models capable of predicting the thermal conductivity profile in the depth direction, and through validation on a synthetic data set, we confirmed that the machine learning model can predict thermal conductivity profiles in the depth direction. These findings validate the potential of our machine learning model for robustly predicting thermophysical properties from measured data.