Wood-based substrates-known for their renewability, abundance, and surface functionalization potential-have recently gained attention as polymers for laser-induced graphene (LIG) synthesis because of their environmentally friendly attributes. These environment-friendly properties also make them pollution-free and easy to dispose of after use. However, the formation of LIG on wood substrates lacks robust theoretical support, and molecular dynamics (MD) simulations, which are a potential theoretical framework, are time-consuming and computationally intensive. Herein, we employed temperature-dependent MD simulations to explore LIG formation on wood-based materials, validating our findings through a comparative analysis with atomic-scale characterization results. To address the high computational requirements of MD simulations, machine learning (ML) models, including long short-term memory (LSTM) networks, support vector regression (SVR), and multilayer perceptrons (MLP), were implemented to extrapolate predictions beyond direct simulation conditions. Each model exhibited high data explanatory power (R values ≥ 0.9), and the computational time was significantly reduced compared to the MD simulations. ML-based predictions revealed a substantial correlation between the temperature and LIG formation extent, establishing an efficient framework for optimizing LIG synthesis from wood-based materials under various laser processing conditions. This framework has considerable potential for applications in energy storage devices, high-sensitivity sensors, and advanced catalytic materials.