Thalassemia is one kind of genetic blood disorder, which caused if the human body cannot produce sufficient hemoglobin. Hemoglobin known to be a very common essential part in anyone's body. RBC’s of the human body do not function efficiently if there is any deficiency of hemoglobin. A small amount of healthy red blood cells circulates in the blood. The oxygen carried by red blood cells used to produce glucose in the cells that used to keep our body properly active. Due to the lack of sufficient healthy RBCs, sufficient oxygen cannot be delivered to every cell of the body, which can be a reason to cause a person to anemia that is responsible to damage organs and lead one to death. In this research, predicting the existence of Thalassemia with ML, an important part of AI has been proposed. Very popular ML algorithms have been implemented on the processed dataset. K-Nearest Neighbor (kNN), Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, Random Forest, Adaptive Boosting (ADA Boosting), Xgboost, Decision Tree, Multilayer Perceptron (MLP) And Gradient Boosting classifier. In this study, out of ten algorithms, ADABOOST algorithm gave the greatest output, which related to accuracy, and it was 100%.