Planarization technologies such as lapping and chemical-mechanical polishing (CMP) are critical in achieving high-precision surface quality in various industrial applications. While predictive models of tool wear and material removal rate have been developed in previous studies, recent advances in digital twins open new possibilities for integrating physics-based and data-driven approaches into a comprehensive decision-making framework. This paper proposes a digital twin-driven methodology for selection of process parameters in planarization technology. The framework combines lapping kinematic models, tribological equations and machine learning methods into a dynamic, adaptive system capable of predicting tool wear, optimizing parameters, and supporting real-time control. Case studies demonstrate the integration of predictive models into the proposed framework. Potential applications, limitations, and future research directions are discussed.