This study investigates the use of clustering analysis techniques for identifying butterfly species based on their morphological characteristics. Butterflies exhibit substantial variation in wing patterns, colors and body size which makes traditional taxonomic identification both time-consuming and error-prone. Clustering analysis provides a data-driven strategy to group individuals into putative species based on similarities in measurable features. By applying multiple clustering algorithms together with appropriate validation methods, this work evaluates the effectiveness of clustering analysis for butterfly species identification and highlights its potential applications in biodiversity research and conservation. Accurate identification of butterfly species is fundamental to biodiversity conservation, ecological monitoring, and environmental impact assessment. This study examines the efficacy of clustering methods for species identification using butterfly image data. Several algorithms, including K-means, hierarchical clustering, spectral clustering, Gaussian mixture models, and DBSCAN, are employed to partition images into species clusters. To represent discriminative visual information, feature extraction techniques such as Histogram of Oriented Gradients (HOG), Gray Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns (LBP) are used to encode wing textures and shape characteristics. The quality of the resulting clusters is assessed by comparing them with known species labels, enabling a systematic evaluation of each method. The results indicate that clustering analysis offers a scalable and promising approach for automated butterfly species identification and biodiversity monitoring, while also clarifying the strengths and limitations of different clustering techniques for image-based species classification.