Melanoma remains the most lethal form of skin cancer despite major advances in targeted and immune-based therapies. Biomarkers now play central roles in diagnosis, risk stratification, therapeutic selection, and disease monitoring; however, their clinical integration remains inconsistent. This review synthesizes the evolving biomarker landscape across genetic (e.g., BRAF, NRAS, KIT, TERT, NF1, CDKN2A), immune (PD-L1, LAG-3, TIGIT, TILs, TMB), proteomic (S100B, MMPs, signaling signatures), and digital/imaging biomarkers (AI-assisted dermo copy, spatial and multiplex profiling). We highlight subtype-specific differences in mucosal, acral, and uveal melanoma, where biomarker patterns and therapeutic responses diverge markedly from those of cutaneous disease. Liquid biopsy approaches, including ctDNA, methylation signatures, and extracellular vesicles, are evaluated for minimal residual disease detection and resistance monitoring. To advance clinical translation, we propose a standardized, stepwise diagnostic therapeutic framework integrating tissue- and blood-based biomarkers with AI-enabled imaging to support personalized management in both adjuvant and metastatic settings. Key translational enablers include assay harmonization (PD-L1, TMB, ctDNA), evidence-tiered validation, and pragmatic clinical trials incorporating biomarker-driven endpoints. Addressing cost, accessibility, and data ethics will be essential for biomarker-guided precision oncology to become a sustainable clinical reality across diverse health systems.