This study reviews the transformative impact of deep learning (DL) in generating synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) datasets, particularly in spine surgery. It explores how DL-driven sCT aims to enhance surgical planning, improve diagnostic capabilities, and potentially integrate with navigation and robotic systems, while also critically evaluating current methodologies, performance metrics, and challenges to widespread clinical adoption. The overarching goal is to reduce patient radiation exposure and streamline clinical workflows by providing CT-equivalent bone visualization from MRI data.