Accurate machinery plays an important role in automotive industries. In the manufacturing landscape of accurate machineries, servo motors and drives are ruling the automation and robotic manufacturing industries. Also, the industry demands for the advancements in accurate control techniques of servo motor which is more powerful to meet the requirements. Due to depreciation and ageing effect, the parameters of DC servo motors change over time. As a result, it must be updated automatically. The characteristics of DC servo motors fluctuate with time as a result of depreciation and the ageing impact. It must therefore be updated automatically while the plant is operating. The system parameters estimation process with high preciseness of a DC Servo motor operating with highly nonlinear relationship is challenging. The estimation process has a significant impact on the accuracy of the controller parameter settings. For the DC servo system to operate well, it is crucial to estimate the precise parameters. This research suggests a data-driven machine learning estimation approach (machine-learning based regression) to address this issue and offer a reliable data base for developing the optimal control strategy for DC Servo motor. This study introduces parameter estimation of a DC servo system, which is used to obtain the precise and trustworthy estimation of parameters. The proposed strategy is easy to use and flexible, therefore it will produce results that are effective and efficient in terms of computing. The significance and effectiveness of the proposed methodology were underlined by contrasting its efficacy with that of the existing methods