<p>The accurate identification of butterfly species is of paramount importance due to their critical role as pollinators and their complex interactions within ecosystems. These insects serve as essential agents in plant reproduction through pollination and are key subjects in ecological research. Their interactions with humans, flora, and fauna can result in both beneficial and detrimental outcomes, making precise species identification crucial yet challenging, typically requiring specialized knowledge. The field has seen extensive research development, particularly in leveraging deep learning technologies for species classification. This research focuses on the classification of nine butterfly species indigenous to Türkiye, utilizing established deep learning architectures including AlexNet, VGG-16, and ResNet-50. The study conducts a comparative analysis of these models' performance metrics. Building upon this analysis, the researchers developed an enhanced model based on VGGNet architecture. This modified approach demonstrated superior performance, achieving 99.34% accuracy on training data and 93.84% accuracy on validation data, significantly exceeding the performance of standard pre-trained models. These findings highlight the effectiveness of specialized deep learning solutions in biological classification challenges. The study makes a significant contribution to the intersection of artificial intelligence and ecological research, providing a robust framework for species identification that can be valuable for both scientific research and conservation efforts. This advancement in automated species recognition represents a meaningful step forward in the application of technology to environmental monitoring and biodiversity assessment.</p>