The present study proposes a unique predictive framework for early skin cancer detection, integrating machine learning (ML), histopathological image analysis, and genomic data processing. The proposed diagnostic platform combines deep neural networks (DNNs), conventional neural networks (CNNs), and quantum-based deep learning architectures, ensuring the diagnostic precision of the analysis using medical images and gene expression profiles. The histopathological images were sourced from the Genomic Data Commons (GDC) portals TCGA_SKCM cohort, containing 923 tissue samples with associated medical metadata, gene profiles (e.g., KIT, BRAF), case ID, and disease subtypes. The DNN model achieved a training accuracy of 99.93%, while the CNN and quantum-based models attained 98.91% and 98.85%, respectively. These models seem to be efficient in dealing with histopathological images and figuring out the critical diagnostic features required for the prediction. However, testing accuracies for DNN, CNN, and quantum models were 47.84%, 50.00%, and 49.19%, highlighting challenges in generalizability. The result suggests the model to be an important step forward in the early skin cancer detection, with a possible benefit of improving patient care during clinical practices.