Silicon carbide-coated carbon fiber-reinforced carbon aerogel (SiC-C/CA) composites are ideal for high-temperature applications due to their ability to endure rapid temperature changes without losing structural integrity. However, assessing and optimizing the Thermal Shock Resistance (TSR) of these composites is challenging due to the complexities in measuring thermal and mechanical responses accurately under rapid fluctuations. Herein, we introduce a novel approach combining the cohesive finite element method (CFEM) with machine learning (ML) to address these challenges. The CFEM simulates crack initiation and propagation and captures mechanical behavior under thermal stress, while ML predicts TSR using simulation datasets, reducing the need for empirical trial-and-error processes. Our method achieves a prediction error for coating residual stress within 15.70% to 24.11% before and after thermal shock tests. Additionally, the ML model, developed to predict the average stiffness degradation factor of the SiC coating after three thermal shock cycles, achieves a coefficient of determination (R²) of 0.9171. This combined approach significantly improves the accuracy and efficiency of TSR assessment and can be extended to other coating materials, accelerating the development of high-temperature-resistant materials with optimized TSR for industrial applications.