<![CDATA[This article presents a comprehensive systematic review of recent advancements in machine learning (ML) applications for diagnosing Thalassemia, a genetic hematologic disorder. Focusing on studies from the last five years, this review highlighted significant technological advancements in ML, including the use of predictive modeling, image analysis, and deep learning algorithms, which have considerably improved the accuracy and efficiency of Thalassemia diagnosis. The review evaluates the application of various ML models in analyzing extensive biomedical data, which significantly enhances patient management and treatment outcomes. Key challenges such as data diversity, model transparency, and the need for robust training datasets are discussed, along with the integration of ML into existing clinical workflows. The potential transformative impact of ML in hematology is underscored, critically evaluating its effectiveness and ongoing developments in the field. This review aims to provide insights into the current research trends and future directions in the use of ML for the diagnosis and management of Thalassemia and other similar hematological disorders.]]>
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