A once rare disease, malignant cutaneous melanoma has evolved to become one of the most fatal forms of cancers, accounting for a global death toll of 50,000 every year. While surgical interventions have shown to increase survival rates for patients with early-stage melanoma, these survival rates decrease as the disease progresses to metastases. Surgical interventions alone are no longer curative, and interventions must include chemotherapies to improve line of defense. However, while therapeutic interventions are available, the efficacy of these treatments can be greatly impacted by the complexity of the disease. When introduced to new metastatic sites, the cancer cells are faced with new environmental conditions, in which sometimes nutrients and oxygen are scarcely available. Through a biological phenomenon referred to as metabolic rewiring, the cancer cells adapt to their new, metabolic changes, promoting its own survival and proliferation under all stressful conditions. While the impact of metabolic rewiring and its influence on treatment efficacy has been established, the direct correlation between the two is not completely understood. The MALMO project (Mathematical Approaches to Modelling Metabolic Plasticity and Heterogeneity in Melanoma) aims to bridge the gap in knowledge within this domain by combining mathematical approaches with state-of-the-art artificial intelligence (AI) methods to automate pathology grading systems, conduct quantitative image analysis (including feature extraction of relevant biomarkers), and develop a model to predict the development of heterogeneity in melanoma tumors. The mathematical model – based on partial differential equations (PDE) – is being designed to account for time, space, and biological scales, such as molecular, cellular, and tissular, in order to understand how melanoma behaves in oxygen and nutrient-deprived tumor microenvironments. The mathematical model is augmented with the help of AI, in which features from hematoxylin and eosin (H&E) and cluster of differentiation 31 (CD31)-stained whole slide images (WSI), as well as Hyperion images, are being extracted and evaluated to assist with the modeling component. In this paper, we introduce the MALMO project and demonstrate some preliminary findings using AI. Understanding this rewiring and developing a prediction model will help to further our understanding of melanoma progression. In doing so, we can begin creating tailored therapies which could better target metastatic melanoma and improve the survival rate for these late stage affected patients.