Big Datasets are becoming more and more prominent in the field of astrophysics with new powerful telescopes and surveys capturing parts of our night sky in an almost continuous fashion. Consequently, unsupervised Machine Learning is key to getting first insight into this abundance of data. We present our results on the performance of unsupervised Machine Learning on the task of classifying RR Lyrae stars into their respective subclasses (RRab, RRc, and RRd) using their lightcurve morphology. We crossmatched the Gaia DR3 RR Lyrae Catalog with the TESS Input Catalog and subsequently extracted a training dataset of over 30000 lightcurves from TESS full-frame images. We further preprocessed and analyzed these lightcurves using a custom-built python-package. We applied a β-Variational Autoencoder (β-VAE) followed by an unsupervised clustering technique to the extracted lightcurves to infer the subclasses based on structure within the data. We additionally discuss the effect of enhancing the light curves with information from the Gaia DR3 Catalog such as effective temperature and metallicity before clustering. We present a β-VAE that is able to depict the distribution of lightcurves in a low-dimensional latent space and artificially generate new lightcurves from said latent space. Furthermore, we present our initial results for the unsupervised classification including a comparison with the Gaia DR3 RR Rylae Catalog. We further discuss our intentions to apply the method to ZTF lightcurves in preparation for the Vera C. Rubin LSST. Our pipeline will be an efficient way of obtaining initial insight into the Vera C. Rubin LSST data.