Introduction: Cutaneous and uveal melanomas present distinct molecular characteristics and therapeutic responses, complicating their clinical management. Artificial intelligence (AI) is increasingly being applied to improve gene therapy and precision oncology strategies, offering promising solutions to overcome treatment resistance and tumor heterogeneity. Methods: This work examines peer-reviewed studies from the past five years, focusing on AI integration in melanoma gene therapy, multi-omics analysis, predictive modeling, and immunotherapy response prediction. Results: AI has shown potential to improve treatment planning by predicting therapeutic response and progression-free survival through radiomics-based models. A novel disulfidptosis-linked signature enhances survival prediction and treatment sensitivity, while HLA-DQA1 has emerged as a promising therapeutic target. Integration of single-cell and bulk RNA sequencing has uncovered key metastasis-related genes, and MethylMix analysis has identified methylation-altered genes affecting expression. In uveal melanoma, AI-driven data fusion linked 48 genes to metastasis, while hdWGCNA enabled mapping of gene modules to specific cell types for refined risk prediction. Machine learning models further optimize prognostic assessments and therapeutic response prediction. A composite decision index incorporating programmed cell death modes improves prognostic accuracy. The immune response score predicts overall survival in cutaneous melanoma, and the AI-derived Stem.Sig signature links cancer stemness to immunotherapy resistance. Deep learning has also been used to analyze TERT promoter mutations for metastatic potential, while a six-gene panel predicts anti-PD-1 therapy response. CSIRG-based models improve patient stratification, and logistic regression reaffirms AI’s prognostic value. Conclusions: Despite the growing success of AI in integrating multi-omics data, enhancing survival prediction, and guiding personalized melanoma therapy, challenges remain. These include limited high-quality, melanoma-specific datasets and algorithmic bias, which may compromise clinical reliability. Overcoming these barriers will require robust data governance, ethical AI model development, and stronger institutional policies.