The most frequent hematological disorder is anemia, which can affect anyone. This illness develops when the blood lacks enough red blood cells or hemoglobin. However, if a person recognizes his anemia relatively early on, the condition may be treatable with the right medication. So, this study has worked for the earliest detection of anemia on various parameters. The study has analyzed the prediction using numerous machine learning and ensemble learning algorithms. The dataset has been sourced from a local pathology center and the dataset has 1000 instances and 8 attributes. The system has predicted the result based on various measures of the confusion matrix. In the end, the greatest result for predicting anemia with 95% accuracy came from the Logistic Regression technique. The main objective of the paper is to determine if a person is anemic or not within a moment by using classification algorithms. So, people have a better solution for the earliest detection of anemia.