Flower classification is a challenging task due to the vast diversity in species, colors, shapes, and sizes. The presence of overlapping visual features further complicates accurate recognition. Traditional classification methods, including manual identification and feature-based machine learning techniques, often struggle to achieve high accuracy and scalability. To address these challenges, this study explores the potential of deep learning for automated flower classification. We perform a comparative evaluation of six prominent convolutional neural networks (CNNs)—MobileViT, EfficientNetV2, InceptionV3, ResNet-50, MobileNetV3, and VGG-16—by assessing their classification accuracy, computational efficiency, and model complexity. Our experiment covers diverse floral datasets, accounting for variations in species, image complexity, and dataset size. The findings offer insights into the advantages and disadvantages of various models and suggest methods to improve their performance in real-world applications. These results demonstrate that deep learning-based approaches significantly improve automated flower classification, offering a robust and scalable solution for various domains, including agriculture, botany, and biodiversity conservation. In addition, the proposed approach can aid farmers in plant species identification, assist botanists in cataloging biodiversity, and serve as an educational tool for nature enthusiasts.