Melanoma, a highly aggressive skin cancer, continues to challenge current therapeutic modalities due to its resistance and high mortality rates. Recent advancements highlight cuproptosis, a copper-driven form of programmed cell death, as a promising target for melanoma treatment. This study integrated machine learning and large-scale genomic data to identify FDX1 as a pivotal gene in cuproptosis-related pathways for melanoma. We developed a novel nanomedicine, ACM@MCHS-CuMOF@Dox, combining Mesoporous Carbon Hollow Spheres (MCHS) loaded with Copper-based Metal-Organic Frameworks (CuMOFs) and Doxorubicin (Dox), to exploit this discovery. The nanomedicine leverages a biomimetic approach by incorporating A375 cell membranes, enhancing tumor-targeted delivery. Physicochemical characterization confirms optimal drug loading and pH/GSH-responsive release profiles. In vitro studies demonstrate that ACM@MCHS-CuMOF@Dox inhibits melanoma cell proliferation, migration, and invasion, outperforming other formulations. Mechanistic investigations revealed that ACM@MCHS-CuMOF@Dox induced robust apoptosis and cuproptosis through FDX1 downregulation, thereby enhancing oxidative stress and therapeutic efficacy. These findings underscore the potential of combining machine learning-driven target identification with advanced nanomedicine for precision melanoma therapy. This approach offers a promising strategy for overcoming current treatment limitations and advancing personalized cancer care.