It demonstrated the feasibility of using IR spectral image data for ML-based chemical classification. Dataset preparation involved extracting spectral images from the SDBS and converting them into high-dimensional vector data. Despite some limitations, including sensitivity to spectral deviations and the need for methodological refinements, unsupervised ML demonstrated good potential for classifying IR spectra. In spectra with low transparency in the fingerprint area and those with large and broad peaks, only the features were emphasized, making it difficult to separate the delicate chemical structures. In addition, chemists make identifications using other incidental information; however, scaling the composition formula and molecular weight and adding them to the elements did not affect the results. This finding revealed that, even after scaling, the 208620-dimensional vector features determined the features, despite making a one-dimensional feature change to the displacement of the 208620-dimensional vector features. Additionally, TC is a useful, albeit sometimes divergent, metric for assessing molecular similarities. Hierarchical clustering of IR spectral data demonstrated that unsupervised ML can be used to classify chemical structures based on spectral features. The main clusters (A-G) exhibited distinct patterns and characteristics, with higher intracluster similarities among some subclusters. These results highlight the potential of unsupervised ML for chemical classification, even though some classifications might not align with traditional chemists' intuitions. Comparison of the clustering results against molecular fingerprints, using the ARI as a metric, demonstrated that the image data provided better classification performance than numerical data of the same resolution. This study also explored the feasibility of employing ML-based image-analysis techniques in materials science to replicate the classifications made by chemists. Existing imagerecognition methods can enhance the data-collection efficiency for exploring unknown materials. Furthermore, the use of images eliminates the need for data processing, such as interpolation. These findings suggest that unsupervised ML using IR spectral images holds significant promise for advancing chemical classification, particularly in cases where traditional methods may be limited. Future studies should focus on refining the sensitivity to spectral deviations, enhancing the algorithm's ability to identify subtle chemical differences, and exploring the integration of additional molecular data for improved classification accuracy. Additionally, further research could investigate the scalability of this approach for large datasets and its potential application in real-time materials discovery and characterization.