Mild to severe anemia is caused by thalassemia, a common genetic disorder affecting over 100 countries worldwide, that results from the abnormality of one or several of the four globin genes. This leads to chronic hemolytic anemia and disrupted synthesis of hemoglobin chains, iron overload, and poor erythropoiesis. Although the diagnosis of thalassemia has improved globally along with the treatment and transfusion support, it is still a major problem in diagnosing in high-prevalence areas like Pakistan. This work aims to assess the performance of numerous combinations of machine learning methods to detect alpha and beta-thalassemia in their minor and major types. These results are obtained from CBC and HPLC analysis. The analyzed models are K-nearest Neighbor (KNN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). The study aims to examine the effectiveness of the developed models in discriminating thalassemia variants, especially in the light of Pakistani patients' data. The study found that XGBoost achieved the highest performance on both the CBC and HPLC datasets, with training accuracies of roughly 99.5% for CBC and 99.3% for HPLC. The test accuracy across both datasets was consistently high and thus the best model for detecting thalassemia in this research study. The imported SVM model, slightly less accurate than XGBoost, still has strong performance, particularly on the HPLC data where the cumulative testing accuracy of the model stood at 99.4%. As can be seen from the results, XGBoost specifically shows a very high accuracy of above 99% in the detection of thalassemia types using CBC and HPLC data for Pakistani patients. To the author's knowledge, this research is the first to predict alpha and beta-thalassemia in its major and minor forms using these diagnostic reports. These models indicate that they can offer significant support in detecting thalassemia in resource-constrained settings such as Pakistan. If deep learning is incorporated, even greater accuracy could be achieved.