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We proposed a machine learning approach to identify and distinguish dustystellar sources employing supervised and unsupervised methods and categorizingpoint sources, mainly evolved stars, using photometric and spectroscopic datacollected over the IR sky. Spectroscopic data is typically used to identifyspecific infrared sources. However, our goal is to determine how well thesesources can be identified using multiwavelength data. Consequently, wedeveloped a robust training set of spectra of confirmed sources from the Largeand Small Magellanic Clouds derived from SAGE-Spec Spitzer Legacy and SMC-SpecSpitzer Infrared Spectrograph (IRS) spectral catalogs. Subsequently, we appliedvarious learning classifiers to distinguish stellar subcategories comprisingyoung stellar objects (YSOs), C-rich asymptotic giant branch (CAGB), O-rich AGBstars (OAGB), Red supergiant (RSG), and post-AGB stars. We have classifiedaround 700 counts of these sources. It should be highlighted that despiteutilizing the limited spectroscopic data we trained, the accuracy and models'learning curve provided outstanding results for some of the models. Therefore,the Support Vector Classifier (SVC) is the most accurate classifier for thislimited dataset.

Spectral identification and classification of dusty stellar sources using spectroscopic and multiwavelength observations through machine learning