Diabetes Mellitus (DM) plays a significant role in increasing the associated health problems worldwide by acting as a Comorbid condition. Moreover, it is a progressive illness without severe external symptoms leading to a fatal impact on the human body if left unnoticed or untreated. This research work aims to associate an individual’s lifestyle and ethnic background in assessing the risk of Diabetes acting as a comorbid condition. A detailed assessment of lockdown impact with rapid modification in individual’s lifestyle due to the pandemic gives specific insight into individuals becoming susceptible to Diabetes Mellitus. An ensemble of ML algorithms is utilized in predicting the risk of individuals turning Diabetic. The ensemble of the ML model is trained on the Pima Indian dataset and Vanderbilt biostatistics diabetes dataset providing the impact of Type 1 diabetes mellitus. The proposed super learner model provides the highest classification accuracy of T1DM & T2DM with 97% compared to an ensemble of algorithms in identifying and classifying the individuals as being susceptible to DM due to the lifestyle and ethnic background.