Autoimmune hemolytic anemia (AIHA) is a rare and challenging autoimmune disorder characterized by the immune system’s attack on red blood cells (RBCs) which is leading to anemia and other health complications. This study investigates the relationships among key hematological parameters which are Hematocrit, Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH) and Mean Corpuscular Hemoglobin Concentration (MCHC)—and their effects on leukocytes, thrombocytes and erythrocytes. The study uses a large secondary dataset of complete blood count samples. It applies multivariate regression and analysis of variance among various blood cell components with the goal to early screening of AIHA. The study also focuses on creating diagnostic models. The findings show that MCHC has a negative link with platelet counts. Hematocrit and MCH have positive links with RBC counts. Challenges such as multicollinearity and non-normal data distributions were addressed through statistical techniques like Variance Inflation Factor analysis. The results highlight the distinct roles of hematological parameters in diagnosing AIHA and suggest that integrating these parameters can improve early detection and treatment strategies. The study has some limitations in model performance for certain blood components. But it provides a strong base for using blood tests to diagnose autoimmune disorders. It highlights the need for better methods and larger datasets in future research.