A critical gap in current efficiency in melanoma patient treatment is the lack of a fully integrated, functional understanding of tumor evolution over time. Recent advances have fundamentally reshaped our understanding of melanoma biology, while increasing clinical complexity has highlighted the need for more comprehensive and biologically informed clinical decision-support frameworks. We propose the implementation of a multimodal disease profiling framework as a core clinical decision-support asset, enhancing treatment optimization across the full disease course in melanoma patients. By integrating proteogenomics, AI-driven digital image analysis, and structured longitudinal clinical metadata, multimodal disease profiling could provide a comprehensive and dynamically evolving view of each patient’s disease. Proteogenomics reveals tumor signaling activity, protein complex dynamics, and emerging therapeutic vulnerabilities that may drive progression and resistance. In parallel, AI-enabled digital pathology analysis characterizes tumor morphology, clonal heterogeneity, and immune context, capturing spatial and functional changes associated with metastatic transition. When combined with longitudinal clinical data, these layers enable patient-specific models tracking tumor evolution, metastasis, and treatment exposure. Leveraging one of the largest melanoma biobank and database resources at the European Cancer Moonshot Center in Lund, our strategy directly addresses the recurrent transition from primary tumors to metastatic disease. This strategy positions multimodal disease profiling as a critical enabler of precision melanoma care by providing biologically grounded, evidence-based decision support, facilitating rapid and structured case assessment through multimodal insights, enabling prediction of treatment response, resistance, and disease trajectory, and supporting adaptive, evidence-informed therapeutic decision-making.