Skin cutaneous melanoma is one of the most aggressive forms of skin cancer, with various prognostic factors that can significantly impact patients’ survival outcomes. Survival analysis helps in identifying key factors influencing patient outcomes and guides in clinical decision-making. In literature, statistical methods have been used for the survival analysis of skin cancer patients but these methods have limitations. To address the limitations of traditional statistical methods in survival analysis, researchers have developed a range of machine learning (ML)-based survival analysis techniques. These ML techniques offer advanced capabilities for modeling complex relationships and improving prediction accuracy. But “black box” nature of ML models poses a challenge, especially in fields like healthcare where understanding the rationale behind predictions is crucial. It this work, Explainable AI (XAI)-based survival analysis has been carried out using XGboost model and clinical features of skin cutaneous melanoma patients. XAI models explain their prediction by showing the important features involved in the prediction to demonstrate their reliability to be used by the clinicians. To validate the performance of XAI model, in this work, multivariate regression-based Cox Proportional Hazard (CPH) model has been developed which shows the relationship of patients’ clinical features and survival time. The proposed XAI-based model has C-index value of 84.3% and shows that age, pathology T stage, and pathology N stage are key factors influencing the survival of skin cutaneous melanoma patients. The CPH model further validates the strong association between these features and patient survival.