Building a comprehensive catalog of galaxy clusters is a fundamental task for studies on structure formation and galaxy evolution. In this paper, we present Cluster Optical Search using Machine Intelligence in Catalogs (COSMIC), an algorithm utilizing machine learning techniques to efficiently detect galaxy clusters. COSMIC involves two steps, the identification of the brightest cluster galaxies and the estimation of cluster richness. We train our models on galaxy data from the Sloan Digital Sky Survey and the WHL galaxy cluster catalog. Validated against test data in the region of the northern Galactic cap, the COSMIC algorithm demonstrates high completeness when crossmatching with previous cluster catalogs. Richness comparison with previous optical and X-ray measurements also demonstrates a tight correlation. Our methodology showcases robust performance in galaxy cluster detection and holds promising prospects for applications in upcoming large-scale surveys. The COSMIC codes are published on https://github.com/tdccccc/COSMIC.

Cone search capability for table J/ApJS/276/21/table1 (Clusters of galaxies identified from the test data)