Radial basis function (RBF) networks, deep neural networks (DNN) with Adam optimization, spline interpolation, polynomial approximation, and DNN with Levenberg-Marquardt (LM) optimization are five sophisticated techniques used in this work to build a novel universal linearization framework. Through adaptive mode selection of the most appropriate technique depending on the unique characteristics of the sensor data, the proposed system achieves higher accuracy and robustness in handling diverse nonlinearities. Experimental results demonstrate remarkable improvement in linearization performance for various kinds of thermocouple sensors, witnessing the usability and efficiency of the framework for real-time applications.