Background: Hematologic malignancies are a group of heterogeneous neoplasms which originate from hematopoietic cells. The most common among them are leukemia, lymphoma, and multiple myeloma. Machine learning (ML) is a subfield of artificial intelligence that enables the analysis of large amounts of data, possibly finding hidden patterns. Methods: We performed a narrative review about recent applications of ML in the most common hematological malignancies. We focused on the most recent scientific literature about this topic. Results: ML tools have proved useful in the most common hematological malignancies, in particular to enhance diagnostic work-up and guide treatment. Conclusions: Although ML has multiple possible applications in this field, there are some issue that have to be fixed before they can be used in daily clinical practice.