Algorithms like Deep Learning (DL) and Machine learning (ML) have been increasingly used in the diagnosis and treatment of leukemia. One of the main applications of ML in leukemia is in the analysis of samples of blood to identify abnormal cells and diagnose the disease. ML algorithms can be trained to recognize patterns in large datasets of blood samples and medical records and use this to predict the presence and progression of leukemia. This can help doctors to diagnose the disease earlier and more accurately and to develop personalized treatment. In addition, ML can also be used to develop predictive models for leukemia treatment outcomes, helping doctors to choose the most effective treatments for a patient depending on his unique medical history. This can lead to better outcomes and fewer side effects from treatment. Overall, ML has revolutionized in the analysis and cure of leukemia and other types of cancer and is already making a significant impact in the field of oncology. ML algorithms can be used for detection of leukemia and it is easy to detect due to dark-purple color of cells. But detecting leukemia without ML techniques is time‐consuming and challenging. Utilization of ML techniques for its diagnosis is increasing day by day. In this, the literature of leukemia, causes, symptoms, different types of leukemia and ML algorithms used for leukemia diagnosis are given.