Multiple sclerosis (MS) is a chronic autoimmune and neurodegenerative disease characterized by marked clinical heterogeneity. While the genetic architecture underlying disease susceptibility is well established, the role of genetic factors in shaping disease prognosis remains clearly defined. In this structured narrative review, we examine available evidence on genetic contribution to key MS prognostic domains. This includes clinical outcomes, such as age at onset, relapse rate, disability progression, neurological sequelae, and cognitive impairment. We also consider radiological measures like brain and spinal cord lesion burden, gadolinium-enhancing lesions, and atrophy, as well as laboratory biomarkers, such as oligoclonal bands and Immunoglobulin G (IgG) index. Overall, current evidence suggests that genetic influences on prognosis are modest and highly heterogeneous. Only a limited number of associations—primarily from genome-wide association studies (GWAS)—have shown consistent replication, whereas many reported findings come from small candidate-gene studies and remain unconfirmed. Among these, the largest GWAS on age-related Multiple Sclerosis Severity Score (MSSS) identified a locus in the DYSF–ZNF638 region reaching genome-wide significance. The strongest evidence from GWAS relates to relapse rate, magnetic resonance imaging (MRI) measures (e.g., thalamic atrophy) and intrathecal IgG synthesis, the latter also reaching genome-wide significance. Interpretation of genotype–phenotype associations is further limited by small sample sizes, limited replication, heterogeneity in study design with the predominance of candidate-gene approaches, variability in outcome definitions, treatment exposure, and population ancestry. These limitations currently preclude the routine use of genetic markers for prognostic stratification in clinical practice. Larger studies and collaborative genetic consortia efforts are needed to improve statistical power and reproducibility. Additionally, emerging epigenetic studies may provide valuable insights into prognosis and disease management. Understanding which genetic factors can predict diverse MS courses could enhance patient management and enable personalized treatment approaches.