Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the network produced discriminative feature vectors that effectively captured the visual attributes of each butterfly. These feature vectors were then used to train a range of machine learning classifiers, including Support Vector Machines (SVM) with different kernels, k-Nearest Neighbors (KNN), Neural Networks (NN), and Stochastic Gradient Descent (SGD). The dataset utilized in this study is designed for identifying butterfly and moth species. It includes 100 distinct category labels. According to these findings, the SVM with a polynomial kernel achieved the best classification accuracy (up to 95%), while the NN followed closely. However, KNN and SGD had somewhat lower accuracy, and external testing of the framework on unknown images was found to be 83.5% on images taken outside the dataset. Hence, the effectiveness of deep learning in feature extraction is demonstrated when combined with machine learning classifiers.