In this paper, we propose a novel learning paradigm called "DeepFlorist" for flower classification using ensemble learning as a meta-classifier. DeepFlorist combines the power of deep learning with the robustness of ensemble methods to achieve accurate and reliable flower classification results. The proposed network architecture leverages a combination of dense convolutional and convolutional neural networks (DCNNs and CNNs) to extract high-level features from flower images, followed by a fully connected layer for classification. To enhance the performance and generalization of DeepFlorist, an ensemble learning approach is employed, incorporating multiple diverse models to improve the classification accuracy. Experimental results on benchmark flower datasets demonstrate the effectiveness of DeepFlorist, outperforming state-of-the-art methods in terms of accuracy and robustness. The proposed framework holds significant potential for automated flower recognition systems in real-world applications, enabling advancements in plant taxonomy, conservation efforts, and ecological studies.