Quasars are known for their stochastic intrinsic variability at several time scales. Large sample studies help to improve the statistics and allow systematic studies of the time scales of variability and their relationship with the physical mechanisms driving these flux changes. With the upcoming large-time domain photometric surveys such as the Vera C. Rubin LSST, we expect to discover at least a few million new AGN. The large number of sources precludes the possibility of spectroscopic follow-up. Candidate confirmation and Photometric redshift estimation present an active research problem. I will present my thesis research results of a novel technique for redshift estimation that couples cosmological time dilation with intrinsic variability for inference. We used Gaussian pro cesses to effectively model the optical light curves of AGN by constraining the structure function as a means to obtain their redshift priors. The redshift priors estimated from variability are then combined with those obtained from SED fitting to assess the impact of variability priors on redshift estimation. I will present a validation of this approach using the real data from SDSS, along with statistical analyses demonstrating improvements in precision, and reduction in outlier fraction, as well as using simulated data to examine the effects of survey cadence and baseline on redshift esti mation accuracy.