Ovarian cancer (OC) is one of the leading causes of death in women, primarily due to late detection. Tissue Microarray (TMA) imaging is an effective diagnostic tool; however, manual analysis requires significant expertise and is prone to errors. This study proposes an approach using Deep Learning (DL) to automate and improve OC subtype classification from TMA images. Given the scarcity of public TMA datasets, we collected and preprocessed 1,526 images, resulting in a comprehensive training dataset of 3,555 images spanning five subtypes: Clear-Cell Carcinoma (CC), Endometrioid Carcinoma (EC), High-Grade Serous Carcinoma (HGSC), Low-Grade Serous Carcinoma (LGSC), and Mucinous Carcinoma (MC). We trained and evaluated five Transfer Learning models (DenseNet121, EfficientNetB0, InceptionV3, ResNet50-v2, and VGG16) on this dataset, yielding training accuracies of 78.93–97.27% and testing accuracies of 71.72–82.40%. EfficientNetB0, the most compact model, achieved the highest accuracy on both training and testing sets. To our knowledge, no previous studies have explored DL-based classification of OC subtypes solely using TMA data, as existing research has relied primarily on Whole Slide Imaging (WSI) data. We examined relevant WSI-based studies, two of which reported slightly higher testing accuracies (84.64% and 87.54%) than ours. However, these results were based on significantly smaller datasets (305 and 500 WSIs) and involved fundamentally different data types. Therefore, such figures should be considered reference values rather than directly comparable benchmarks. Overall, our results demonstrate the potential of DL to streamline TMA image analysis, reduce expert workload, and improve diagnostic accuracy.