Diabetes Mellitus is one of the chronic diseases affecting the world’s population. The development of diabetic patients is expanding step by step because of the ways of life. It is a significant issue influencing an excess of individuals today, and if it is left unchecked it can create enormous implications on the health of the population. Hence, diagnosing diabetes is extremely fundamental to spare human life from diabetes. Among the different non-invasive methods of finding, breath examination exhibits a simpler, increasingly precise and suitable technique in giving extensive clinical consideration to the illness. It is a well-known fact that Acetone focus in breath has an immediate connection with blood glucose level. The grouping of acetone levels in breath for monitoring blood glucose levels and is possible to predict its values with the use of feature extraction and classification techniques in the machine learning. The paper reviews different methodologies used to identify the presence of acetone in breath samples. Also, the various sensors technologies used in computing the acetone in breath are reviewed.

Acetone, Blood Glucose Level, Breath, Sensors

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