###### Abstract  
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 (R\({}^{2}\)) 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. thermal shock resistance; SiC coating; carbon aerogel; finite element model; machine learning 2024231020242310 damage propagation pathways, making it challenging for current testing methods to fully capture the extent of damage in ceramic coatings. The finite element method (FEM) is one well-established way to study the thermal shock cracking process. Researchers have explored the crack propagation behavior in ultra-high temperature ceramics with sharp leading edges and thermal barrier coatings under thermal shock conditions [11; 12; 13]. Furthermore, investigations into functionally graded materials (FGMs) have focused on fatigue crack growth under cyclic thermal loading and dynamic crack behavior under thermal and cold shock conditions [14; 15; 16; 17; 18; 19]. These investigations provide insights into the stress distribution, crack initiation, and propagation mechanisms, contributing to a deeper understanding of the materials' performance under extreme conditions. However, the application of FEM to evaluate the thermal shock resistance (TSR) of coating materials remains relatively rare. This gap highlights the need for further research to develop and refine FEM-based models that can accurately predict and optimize the TSR of advanced coating systems. In recent years, machine learning (ML) has emerged as a crucial tool for optimizing material properties by integrating advanced algorithms with materials science knowledge [20; 21; 22; 23]. For instance, through ML-guided approaches, researchers were able to produce alloys with hardness values 10% higher than those in the original training dataset using only a limited number of experiments, illustrating the role of ML in modeling complex relationships between composition, microstructure, and mechanical properties to develop superior materials [20]. Similarly, a combination of ML and high-throughput experimentation facilitated the rapid screening of ultra-hard high-entropy ceramic coatings, achieving a 5% improvement in hardness over the best-known values from the dataset [21]. Additionally, ML techniques have been employed to optimize the heat treatment parameters for alloys at high temperatures. By integrating ML with high-temperature alloy creep theory, researchers significantly enhanced creep resistance through optimized heat treatment processes [22]. Moreover, using algorithms such as XGBoost, AdaBoost, random forests, and k-nearest neighbors, researchers can accurately forecast material performance from limited experimental data, demonstrating ML's capability to extract valuable insights from small datasets [20; 21; 23].