This work explores the application of Survival Analysis in the context of hematopoietic stem cell transplantation for multiple myeloma to enhance the predictive capacity and interpretability of transplant outcomes and inspect the patients’ overall survival. Our methodology uses all the proposed Survival Analysis models. These models are used to conduct a feature importance analysis and do some survival predictions with the interpretation of patient outcomes. The dataset, comprising 254 instances and 15 attributes, includes medical information collected from multiple myeloma patients before hematopoietic stem cell transplantation procedures. The primary objective of this work is to assess the robustness of Survival Analysis models with our data based on the concordance index metric. Through feature importance analysis, it has been revealed that variables such as the International Staging System, treatment lines, age, and disease relapse play pivotal roles in determining patient survival post-transplant. Survival predictions have been conducted for three distinct cases from the dataset, evaluating the risks patients may encounter following their treatments. These results have been validated by healthcare professionals, underscoring the reliability and applicability of this study’s findings in medical scenarios.