Finite Mixture Model serves as a potent statistical tool for modeling intricate data distributions through the amalgamation of multiple Gaussian components. The aim of this work is to use finite mixture of normal distribution to model data on diabetic patients from Modibbo Adama University Teaching Hospital, Yola, Adamawa State, so as to expose latent sub-groups. The model is first used on synthetic data and subsequently on diabetic patients data. The diabetic data set consists of 553 cases of diabetes mellitus. The variables measured: Age(years), Mass of a patient(kg/meters), glucose level (plasma glucose con- centration, a 2-hour in an oral glucose tolerance test), pressure (Diastolic blood pressure mmHg), insulin (2-hour serum insulin mu U/ml) and class variable (0 or 1) treating 0 as false or negative and 1 treated as true or positive test for diabetes. The synthetic data is obtained from Gaussian distribution. The results showed that there is presence of heterogeneity in the data by showing the presence of 2 sub-groups.