Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. While histopathological image analysis plays a key role in breast cancer diagnosis, the complexity and heterogeneity of these images present significant challenges. To address this, we propose DenseNet-FPA, a novel method that combines Dense Convolutional Networks (DenseNet) with the Flower Pollination Algorithm (FPA) to enhance feature selection and classification in breast cancer histopathology images. DenseNet effectively extracts hierarchical features, while FPA optimizes the selection of the most discriminative features, improving classification accuracy and reducing computational overhead. We evaluated our approach on two publicly available datasets, i.e. BreakHis and BACH, achieving accuracies of 99.32% and 96%, respectively. These results signficantly outperform state-of-the-art methods, which achieved <99% on BreakHis and <93% on BACH. Our findings demonstrate significant improvements in binary classification performance, offering a more efficient and accurate approach for breast cancer diagnosis. The proposed method has potential applications in other histopathological analyses and clinical settings.