## 1 Introduction
In the context of crack propagation prediction, these models have demonstrated effectiveness in capturing complex relationships between crack behavior and influencing factors. For instance, Ref. [1] employed Ridge regression and K-Nearest Neighbors to predict crack growth in polymer materials. The models exhibited high accuracy and provided valuable insights into the crack propagation process. Several studies have demonstrated the effectiveness of machine learning models for crack propagation prediction, even with small and moderate datasets. For instance, Ref. [14] utilized Random Forest and Support Vector Regression models for predicting crack propagation in concrete structures with a small dataset. Their results showed that both models achieved high prediction accuracy and outperformed traditional analytical methods. Similarly, Ref. [15] employed a Gradient Boosting Machine model for predicting crack growth in metallic materials with a moderate dataset. Their findings showed that the model achieved good accuracy and provided valuable insights into the underlying crack behavior. These studies suggest that machine learning models can effectively predict crack propagation behavior, even with small or moderate datasets. By identifying the most important factors influencing crack behavior and accurately predicting crack growth, these models can aid in the design and development of more reliable and durable structures. In this study, we build upon the existing research by comparing the performance of these machine-learning models for predicting crack propagation in composite, metal, and polymer materials. We utilize experimental data obtained from crack propagation tests performed under varying load and temperature conditions. The performance of each model is evaluated to identify the best-performing models for each material type. The proposed study expands upon previously conducted research and significantly contributes to the current state of the art in crack propagation prediction in several means. The study focuses on crack propagation prediction in three distinct types of materials: composite, metal, and polymer. While previous research has often focused on individual material types, this study provides a comprehensive comparative analysis across multiple materials. By examining crack propagation in different materials, the study offers valuable insights into the varying behaviors, influencing factors, and predictive models specific to each material type. This expanded scope enhances our understanding of crack propagation across a wider range of materials and aids in the development of more versatile and accurate prediction models. The study investigates crack propagation under dynamic load and temperature conditions, which are critical factors influencing crack growth in real-world scenarios. While previous research has often focused on static load conditions, this study expands the understanding of crack propagation by incorporating the effects of dynamic loading and temperature variations. By considering these realistic operational conditions, the study enhances the applicability and relevance of crack propagation predictions to practical engineering scenarios. The proposed study expands upon previous research by conducting a comparative analysis across multiple materials, evaluating various machine learning models, considering dynamic load and temperature effects, validating with experimental data, and emphasizing practical implications for structural design. These contributions enhance our understanding of crack propagation prediction and offer practical tools and insights to improve the reliability and durability of structures in various material systems. The study's findings also could have respective practical implications and applications such as:  
1. Risk Assessment and Maintenance Strategies: The study's findings enable better risk assessment and the development of proactive maintenance strategies.