Abstract: Strong gravitational lensing, where a massive galaxy bends and magnifies the light from a more distant source, offers a unique window into the underlying cosmology of the universe and serves as a powerful tool for studying dark energy. Yet identifying and analyzing these rare systems within the vast datasets produced by modern surveys remains a major challenge. In this talk, I will present my thesis work developing new machine learning and hybrid techniques to make strong lens discovery and modeling more efficient. First, I introduce a machine learning–based search that uncovered hundreds of strong lensing candidates in the Dark Energy Survey. Next, I compare three independent machine learning searches applied to the same dataset and show how combining them improves performance and reduces missed discoveries. Finally, I describe a new pipeline that integrates machine learning with traditional parametric modeling, dramatically reducing the time required to model individual systems while maintaining accuracy. Together, these projects provide scalable, automated approaches for discovering and characterizing strong gravitational lenses, maximizing their potential as cosmological probes in the new era of large-scale surveys such as LSST and Euclid.