Multiple myeloma (MM) is a hematological cancer associated with abnormal plasma cell proliferation. Its diagnostic process is long because it is very difficult to discover it at an early stage. This paper presents an approach to aid in MM diagnosis and staging. Tree-based ensemble learning methods are used to measure the features importance in models constructed for predicting MM stages. Comparative analysis showed that random forest outperformed other algorithms with an accuracy of over 97%; however, XGBoost gives a ranking of features considered most prognostic for MM staging. A discussion of results with specialists in hematology supported and validated our proposed study.