Phase transitions in condensed matter systems traditionally require prior knowledge of order parameters for identification. We present Prometheus, a variational autoencoder framework for unsupervised discovery of phase transitions and order parameters in the two-dimensional Ising model without prior physical knowledge. Our approach combines convolutional neural networks with beta-variational autoencoders to learn compressed representations that naturally separate ordered and disordered phases. Experimental validation demonstrates automatic discovery of the order parameter with 0.85 correlation to theoretical magnetization and critical temperature detection within 0.27% of the theoretical value, achieving 89% improvement over principal component analysis while requiring no supervision.