 Anemia is a condition characterized by low levels of red blood cells leading to fatigue, weakness, and other physical symptoms. There are many types of anemia, each requiring its own specific treatment. To diagnose anemia, doctors use a simple blood test called a complete blood count, CBC. However, this test does not differentiate between the various types of anemia, so additional tests may need to be done to determine the exact cause. Additionally, the cost of these tests can be prohibitive in areas where resources are limited. To address this issue, researchers have developed a new model that uses historical data to accurately predict the type of anemia present in patients. This model is based on the extreme learning machine algorithm and achieved an accuracy rate of 99.21%, sensitivity of 98.44%, precision of 99.3%, and an F1 score of 98.84%. This article was offered by Dimas Keralaktisaputra, Cameron Sunit, and Tri Ratnaninsee.