This study explores predictive modeling of immunotherapy response in melanoma by integrating spatial omics profiling with machine learning. The research focuses on identifying spatial biomarkers that predict responses to immune checkpoint inhibitors. Utilizing imaging mass cytometry data, the study refines cell typing techniques and examines the spatial heterogeneity of tumor cell populations. Key findings include the identification of specific cell phenotypes and immune cell infiltration patterns that correlate with treatment outcomes. A random forest model was developed to predict immunotherapy response, highlighting a promising approach for enhancing precision oncology and personalizing treatment strategies.