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The Magnetospheric Multiscale (MMS) mission has probed Earth's magnetosphere, magnetosheath, and near‐Earth solar wind for over 8 years. We utilize an unsupervised learning algorithm, Gaussian mixture model clustering, along with feature generation and simple post‐cleaning methods to automatically classify 8 years of MMS dayside observations into four plasma regions (magnetosphere, magnetosheath, solar wind, and ion foreshock) at 1‐min resolution. With these plasma regions distinguished, we have also identified boundary surfaces (e.g., magnetopause, bow shock). We validate our results on manually generated and rule based region labels described in the literature. We report overlap rates in our cluster determined magnetopauses and bow shocks against Scientist‐in‐the Loop (SITL) identified transitions and published databases. Our features are general and our model is extensible, potentially making it applicable to observational data from multiple other missions. Key Points: We use an extensible unsupervised Gaussian mixture model (GMM) algorithm to automatically classify 8 years of dayside magnetospheric multiscale (MMS) plasma dataOur model distinguishes between solar wind, ion foreshock, magnetosheath, and magnetosphere with 97.8% accuracy compared to manual labelsWe provide classified labels and transitions at 1‐min resolution and stable region specific lists for all 8 years of dayside MMS data [ABSTRACT FROM AUTHOR]