This talk looks at "photomorphic" redshift estimation of galaxies. Spectroscopic measurements and color information (photometric) of the galaxies have been used as redshift predictors. This new approach additionally leverages the galaxy's morphology information. Astronomers explore the cosmos not just by observing through the tiny visible window used by our eyes, but also by exploiting the entire electromagnetic spectrum, from radio waves with wavelengths larger than a house to gamma rays with wavelengths 1,000 times smaller than a proton. This has generated a treasure trove of terabytes of open data related to large-scale wide and deep, both ground and space-based, surveys of the different regions of the sky at multiple wavelengths. Machine Learning (ML) is increasingly being employed to develop robust and scalable algorithms and tools to analyze and mine these images and their associated catalogs to determine patterns and trends and solve science problems of this nature. Bayesian Network and GLM algorithms have been applied with promising results. Our view of the universe is closely tied to our understanding of galaxy formation. This work is a step in that direction using the available "smart data" using "intelligent techniques".