Acute lymphoblastic leukemia (ALL) is a form of leukemia depicted by the rapid proliferation of adolescent white blood cells (WBCs) in the bone marrow. The incidence rate of ALL is approximately 80% in children and 40% in adults. This condition disrupts the production of normal cells, causing neurological abnormalities and potentially leading to fatal outcomes. Consequently, prompt and precise diagnosis is crucial for efficient therapy and recovered survival rates. This study investigates the efficacy of various machine learning models, including XGBoost, Adaboost, LightGBM, and CatBoost, in the prediction of Acute Lymphoblastic Leukemia (ALL). Through comprehensive data preprocessing, feature selection, and model tuning, we assess the performance of these models' using accuracy, precision, recall, and F1 score metrics. Our findings reveal that XGBoost significantly outperforms the other models, achieving the highest accuracy (99.31 %), precision (95.63 %), recall (98.01 %), and F1 score (99.35%). LightGBM also demonstrates strong performance, particularly in precision and recall, making it a viable alternative. Although Adaboost and CatBoost provide satisfactory results, they lag behind XGBoost and LightGBM. This study underscores the importance of selecting advanced machine learning algorithms to enhance diagnostic accuracy for ALL, ultimately contributing to improved patient outcomes through more precise and reliable detection methods. © 2024 Elsevier B.V., All rights reserved.