In this study, we presented a systematic workflow to generate machine learning potentials for two-dimensional materials. We utilized these potentials to calculate thermoelectric properties of these materieals, which requires the computation of higher-order force constants, making it a challenging application for machine learning potentials. We demonstrated that the potentials we trained are as reliable as the reference DFT calculations for thermal property calculations while having orders of magnitude lower computational costs. On average computational time of force calculations with DFT takes approximately 1 hour per structure on a node with 40 cores, while it is only 0.3 seconds on one core with GAP. This speedup allows for longer and larger MD simulations to be run at DFT accuracy. We verified the accuracy of such MD simulations by comparing PDOS calculations with autocorrelation data based on GAP and static Phonopy calculation with DFT. The excellent agreement demonstrates the utility of machine learned potentials is not limited to static properties (i.e., single-step force calculations), and can be reliably used for computing dynamic properties through molecular dynamics simulations. Final GAP models and all the training and validation data were uploaded to a GitHub repository.