Multiple sclerosis (MS) represents a complex, immune-driven CNS condition marked by significant clinical and biological diversity, which complicates diagnostic and prognostic efforts. In recent years, the paradigm of MS research has evolved through rapid biomarker breakthroughs, transitioning from a reliance on traditional neuroimaging to holistic molecular and multi-modal profiling. Liquid-based indicators, such as glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and chitinase-3-like protein 1 (CHI3L1), have emerged as robust correlates of neuroaxonal damage and astrocytic involvement. Parallel to this, novel imaging features notably paramagnetic rim lesions and the central vein sign have increased diagnostic precision. Furthermore, the integration of multi-omics including genomics and metabolomics allows for a more granular understanding of the immune and degenerative pathways in MS. By leveraging systems biology and machine learning, researchers can now identify synergistic biomarker signatures that surpass individual markers in forecasting disease activity and therapeutic outcomes. However, achieving precision neurology requires overcoming obstacles in assay harmonization and clinical validation.