The document describes an automated process developed to efficiently search for globular clusters (GCs) in images of galaxies from the Next Generation Virgo Cluster Survey. Key aspects of the automation include: 1) Using convolutional neural networks (CNNs) to classify image quality with 80% accuracy, removing subjective human judgments. 2) Automating GC identification based on concentration factors and colors from source extraction software. 3) Testing on over 1,000 galaxy images, resulting in a 91% reduction in analysis time and discovery of 5 new GCs compared to manual methods. The automation significantly advances GC detection in an objective, efficient manner.