Melanoma is an underserved area of cancer research, with little focus on studying the effects of tumor extracellular matrix (ECM) properties on melanoma tumor progression, metastasis, and treatment efficacy. We’ve developed a Raman spectral mapping-based in-vitro screening platform that allows for nondestructive in-situ, multi-time point assessment of a novel potential nanotherapeutic adjuvant, nanoceria (cerium oxide nanoparticles), for treating melanoma. We’ve focused primarily on understanding melanoma tumor ECM composition and how it influences cell morphology and ICC markers. Furthermore, we aim to correlate this with studies on nanotherapeutic efficacy to coincide with the goal of predicting and preventing metastasis based on ECM composition. We’ve compiled a Raman spectral database for substrates containing varying compositions of fibronectin, elastin, laminin, and collagens type I and IV. Furthermore, we’ve developed a machine learning-based semi-quantitative analysis platform utilizing dimensionality reduction with subsequent pixel classification and semi-quantitation of ECM composition using Direct Classical Least Squares for classification and estimation of the reorganization of these components by taking 2D maps using Raman spectroscopy. Gaining an understanding of how tissue properties influence ECM organization has laid the foundation for future work utilizing Raman spectroscopy to assess therapeutic efficacy and matrix reorganization imparted by nanoceria. Specifically, this will allow us to better understand the role of HIF1a in matrix reorganization of the tumor microenvironment. By studying the relationship between substrate modulus and nanoceria’s ability to inhibit an ECM that is conducive to tumor formation, we endeavor to show that nanoceria may prevent or even revert tumor conducive microenvironments.