Artificial intelligence (AI), a revolutionary technology, has changed the way of thinking, designing, and implementing any aspect of life. The healthcare industry is also convinced with this new technology in resolving the complex issues related to cancer, cardiology, neurology, and many others. Acute myeloid leukemia (AML) is a type of cancer that develops when the bone marrow creates an excessive number of aberrant myeloblasts or white blood cells. Traditional methods to diagnose AML involve physical examination, blood tests including a peripheral blood smear and complete blood count (CBC), and a bone marrow aspiration and biopsy to check for aberrant blast cells. However, a number of factors could make this difficult, such as the need for specialized tests, like bone marrow aspiration and biopsy, the variability in presentation depending on the AML subtype, the possibility of false negative results on initial blood tests, and nonspecific symptoms that can mimic other conditions. All of these factors could cause delays in diagnosis and treatment initiation. Forthwith, artificial intelligence can be used to detect AML more accurately, with less time needed for diagnosis, and with faster, less expensive, and safer diagnostic services. The application of AI in oncology is not without its difficulties, though, such as human biases and data-related issues. These difficulties may have an impact on how well AI functions in practical situations. This chapter rolls out the use of artificial intelligence (AI), particularly through machine learning algorithms, in diagnosing acute myeloid leukemia (AML) by analyzing medical data, like bone marrow images, flow cytometry results, and clinical parameters.