Homocysteine is a critical blood biomarker for assessing cardiovascular and neurological health risks. Homocysteine levels higher than 15 micromoles per liter are considered indicative of hyperhomocysteinemia. This preliminary study aims to develop a machine learning model to predict homocysteine levels. We used a real dataset composed of 324 non-hyperhomocysteinemia instances and 24 hyperhomocysteinemia instances. Variables carry anthropometric, biochemical, and hematological information. The exploratory analysis revealed the need to address missing data, outliers, and data normalization. The multiple regression model was created using both raw data and preprocessed data. The model performance metrics show improved predictive accuracy of the model using preprocessed data over that using raw data. This highlighted the importance of careful data preparation in enhancing model performance.