Introduction: Diabetes mellitus is the kind of metabolic disease that affects millions of people around the world. Diabetics must have a constant awareness of their blood glucose level to keep their physics in healthy condition. Objective: In this paper, a comprehensive discussion on current methods which are performed for blood glucose measurement and exploration on the current implementation of noninvasive glucometer strategies through linear regression is represented. Methods: Linear regression models are generated through One-to-One and weighted average correlation of individual input parameters. Thirty-eight samples are incorporated in the analysis, here Density in __ampersandsignlsquo;kg/m3 __ampersandsignrsquo;, Pressure generated by Blood Test in __ampersandsignlsquo;Pa__ampersandsignrsquo; and Absorption in __ampersandsignlsquo;AU__ampersandsignrsquo; is considered as Input parameters and Glucose Concentration in __ampersandsignlsquo;mg/dL__ampersandsignrsquo; as an output parameter. R-Square is performed to understand the best fitting between observed and predicted Glucose Concentration levels. In the end, the weighted average prediction is also done for the correlation. Results: By comparing the observed value, the linear regression predicted the optimal glucose concentration (mg/dl) with 0.9794 R-Square value. And from the prediction process, it was observed that Density to Glucose Concentration result with 100% best fitting, Pressure to Glucose Concentration result with 99.99% best fitting, Absorption to Glucose Concentration result with 97.95% best fitting and Weighted Average to Glucose Concentration result with 99.8% best fitting. Conclusion: Arrived direct correlations are very much useful to create new products for monitoring blood glucose level continuously under non-invasive conditions.