Locating the boundary of the Asian summer monsoon anticyclone (ASMA) in the upper troposphere-lower stratosphere (UTLS) (350 K–410 K potential temperature) is important for analyzing the transport of pollutants by the ASMA into the stratosphere. The definition of the ASMA boundary highly impacts the information about the anticyclone's behavior and affects the results when studying its spatio-temporal variability in particular regarding inferred trends over the last decades. In this work, we quantify the differences between methodologies based on potential vorticity (PV) and the Montgomery streamfunction (MSF) used to determine the location of the ASMA boundary. In addition, we apply the unsupervised machine learning spectral clustering method which combines both the PV and MSF fields to derive the location of the boundary. By analyzing the centroid position and boundary geometry for July and August 2023, we demonstrate that at 380 K, the PV-based, MSF-based, and spectral clustering methods exhibit good agreement during the Asian summer monsoon season. We find that the MSF-based method tends to incorporate the eastward-shed eddy over the Pacific around 140° E into the main anticyclone, whereas the PV-based method tends to separate it from the main body. Spectral clustering, however, can exhibit either behavior during the season. Finally, we compare the derived boundaries with satellite measurements of carbon monoxide, showing that the PV-based and MSF-based methodologies successfully enclose the core of elevated carbon monoxide levels as effectively as the spectral clustering method.