This study introduces an automated approach for identifying the tip of the red giant branch (TRGB) in globular clusters, combining astronomical data with algorithmic methods. Using a dataset of 160 globular clusters and Python scripts, we matched stellar sources with Gaia data. Our script generates color-magnitude diagrams (CMDs), and uses the local outlier factor (LOF) algorithm to remove outliers. Applying a second-degree polynomial to fit red giant branch (RGB), we identify the TRGB as the star closest to the fitted curve’s endpoint. By this methdod, we expanded TRGB samples in global clusters to 91 with newer observa tional data. Our results show a decreasing trend in I-band luminosity for metallicities greater than −1, consistent with previous studies. The results show a robust trend fitting and the MI of TRGB is about −4.02 with extremely low metallicity. Our approach enhances TRGB identification efficiency while providing valuable insights for developing automatic tools in astronomical data analysis.