Plant species classification accuracy is crucial for biodiversity conservation and ecosystem monitoring. Traditional taxonomy-based methods, which rely heavily on expert analysis, can be inefficient and prone to errors, particularly when processing large datasets. This study leverages deep learning and machine learning techniques to automate plant species identification, with a strong focus on leaf vein morphology analysis. The proposed approach begins with preprocessing leaf images by converting them to grayscale, extracting significant structural features, and skeletonizing vein patterns. Key morphological characteristics, including vein distributions, textures, and geometric attributes, are then used as input for classification models. They use both sophisticated deep learning models like Convolutional Neural Networks (CNN) and more traditional machine learning approaches like Random Forest (RF), k-Nearest Neighbours (kNN), and Support Vector Machines (SVM). The Xception architecture, known for its depth wise separable convolutions, is particularly effective in capturing intricate vein structures, enhancing classification accuracy. This automated system reduces the dependency on manual identification efforts, making it scalable for large-scale biodiversity research. By integrating deep learning-driven analysis, the proposed framework provides a robust and efficient solution for plant species classification, aiding conservation initiatives and ecological studies.