Photometric redshift estimation (photo-z) offers a scalable alternative to spectroscopic measurements, at the expense of reduced accuracy and increased uncertainty. In this work, we investigate machine learning approaches that explicitly incorporate uncertainty quantification into photo-z estimation using Sloan Digital Sky Survey (SDSS) data. Starting from a baseline neural network, we extend to Gaussian regression, quantile regression, and conformalized quantile regression (CQR). Our experiments demonstrate that, while the baseline model achieves competitive accuracy, uncertainty-aware methods provide more reliable and interpretable predictions.
