This paper introduces a groundbreaking framework that establishes a cohesive link between first principles-based simulations and circuit-level analyses using a machine learning-based compact modeling platform. Beginning with meticulous atomistic simulations, the framework explores the microscopic intricacies of material behavior, serving as the foundational layer for subsequent stages. The generated datasets, enriched with molecular insights, undergo a transformative process guided by machine learning algorithms, enabling the discernment of complex patterns and relationships. As these machine-learning models mature, they evolve into powerful tools capable of predicting intricate behaviors beyond the reach of conventional modeling and simulation approaches. Applied to circuit simulation, the framework excels in providing a holistic understanding of electrical interactions, significantly enhancing accuracy, and expediting design automation. As a proof of concept, we perform first principles-based simulations of the Graphene Nanoribbon Field Effect Transistor (GNRFET), an exploratory device, and create a symbolic-regression-based machine learning model that can readily be integrated into advanced circuit simulation. This framework presents a versatile template offering a unified approach that synergizes the strengths of first principles-based simulations and circuit-level design tools.