The integration of artificial intelligence (AI) into medical diagnostics is transforming the landscape of healthcare, particularly in hematology. AI technologies, leveraging advanced machine learning algorithms and big data analytics, are revolutionizing the diagnosis of hematological disorders such as anemia, leukemia, and lymphoma. This review explores how AI enhances diagnostic accuracy, efficiency, and patient outcomes by processing complex datasets and identifying patterns beyond human capability. AI-driven advancements in hematology include innovations in image analysis, genomic data interpretation, and predictive modeling. Convolutional neural networks analyze blood smear images with high precision, detecting subtle morphological abnormalities and classifying blood cells. Machine learning models interpret genomic data, identifying genetic mutations linked to specific disorders, which is crucial for diagnosing hereditary blood conditions and cancers. Predictive modeling, based on historical patient data, forecasts disease progression and treatment responses, enabling personalized patient management. Despite the promising benefits, the implementation of AI in hematological diagnostics faces challenges such as ensuring data quality and integration, addressing ethical and regulatory concerns, and maintaining transparency and accountability of AI algorithms. Ongoing research and collaboration between clinicians, data scientists, and regulatory bodies are essential to advance AI capabilities and ensure safe and effective solutions. As AI continues to evolve, its integration into hematology holds significant promise for improving diagnostic practices and patient care.