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In this article, the possibilities of accurate prediction of wide range of parameters and optimizing the same through machine learning (ML) approach have been demonstrated for the multi stacked nanosheet transistor (NSFET). The machine is trained by the generated data of the tedious calibrated technology computer aided (TCAD) simulations. An innovative strategy is employed that combines ML with device simulations. Numerous devices are simulated with different geometric parameters like height, width, length and equivalent oxide thickness of the channel. The input, output, and CV characteristics are extrapolated from the simulation which is predicted by ML models. The DC, Analog and RF parameters are derived with a domain expertise approach. The device parameters are anticipated with actual values by the ML approach. Random forest regression, linear regression, polynomial regression and decision …