Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabolism and become chronic upon negligence. Diabetes is one of the key factors that is interlinked with a plethora of diseases. Health management can be achieved through balanced diet, physical exercise, and periodic examination of blood glucose level and hematocrit volume. Our study developed a model to estimate the hematocrit volume (red blood cells) from the correlation of the glucose concentration obtained from a glucometer by employing machine learning techniques. This Article explores the prediction of hematocrit volume in whole blood by applying various machine learning (ML) models such as linear regression (LR), support vector regressor (SVR), decision tree (DT), random forest regressor (RFR), artificial neural network (ANN), and extreme gradient boosting regressor model (XGBoost). We used amperometric signals generated from an electrochemical glucose sensor or glucose strip, which produces current values on glucose concentration. We estimated the hematocrit volume via processing of the amperometric signals to enhance diagnostic capabilities with the least error in the field of biomedical signal processing. The ML models were trained on the data set comprising 80% training set and 20% testing set in the Python programming language. The models were evaluated based on the metrics such as R-squared (R