Background: Metastatic melanoma presents clinical challenges due to tumor heterogeneity and resistance mechanisms, especially when standard treatments fail. Recent advances in artificial intelligence (AI) and spatial proteomics have opened new avenues for precision oncology. Exploring these principles, we report the development and clinical application of a novel integrative workflow combining AI-based digital pathology with spatial proteomic profiling to support personalized treatment strategies. Methods: High-resolution H&E images were used to create and validate an AI model able to uncover subtle morphological patterns and identify different subpopulations of melanoma cells and stroma components. Laser microdissection was then used to isolate clinically relevant regions, and MS-based proteomics was applied to map the spatial proteome signature across different regions. This workflow was applied to a challenging clinical case involving a young patient with recurrent melanoma presenting with multiplex metastases. In addition to the primary tumor, lung and brain metastases were analyzed. Results: Our AI method unveiled two distinct cell subpopulations (PT1 and PT2) spatially separated within the primary lesion. Spatial proteomics further revealed inter-tumor heterogeneity between the primary tumor and metastatic lesions, with increased kinases correlated to target drug resistance. Additionally, upregulated glycolytic signaling and mitochondrial metabolism were identified as key drivers of melanoma progression in this patient. Notably, the AI model also pinpointed PT1 as the likely driver of metastasis, based on strong morphological similarities to the cells found in both brain and lung lesions. This was further supported by spatial proteomic data, which showed a close molecular match between PT1 and the metastatic sites. Conclusions: The findings suggest that targeted therapies may provide limited benefit on their own, while the combination with metabolic inhibitors could represent a more effective treatment option for the future. This case sets the ground for omics analysis inside clinical surroundings, showing how integrating AI-based pathology with spatial proteomics can uncover metastatic drivers and support personalized cancer care in the real world.