The tumor immune microenvironment (TIME) plays a crucial role in the progression and treatment response of ovarian cancer. Understanding the spatial distribution and phenotypic characteristics of immune cells within the tumor microenvironment is essential for developing effective therapeutic strategies. In this study, we demonstrate the utility of SpaFlow in (i) analyzing a tissue microarray (TMA) cohort of ovarian tumors, and (ii) categorizing cellular compositions of TIME, and (iii) validating our findings against immunohistochemistry (IHC)-based evaluations of CD8+ tumor-infiltrating lymphocytes (TILs). SpaFlow is an efficient, customizable pipeline for unsupervised clustering and classification of MxIF data, implemented using Nextflow. SpaFlow performs quality control, clustering, and post-clustering analysis on segmented and quantified MxIF data, facilitating reproducible and scalable analyses across various computing platforms. The SpaFlow pipeline integrates three clustering and classification packages—Seurat, SCIMAP, and CELESTA—each providing unique methodologies for identifying cell types based on phenotypic markers. A novel “meta-clustering” approach condenses clusters across multiple regions of interest (ROIs) into common meta-clusters, streamlining the cell type identification process in large datasets.