## 1 Introduction  
The increasing demand for sustainable and cost-effective asphalt pavement solutions has driven significant research into incorporating reclaimed asphalt pavement (RAP) into new mixtures. The reuse of RAP reduces dependency on virgin materials and minimizes construction waste, contributing to environmental sustainability. However, determining the ideal combination of RAP and foamed bitumen content (FBC) is important to ensure optimal pavement performance, particularly in terms of rutting resistance and tensile strength. The use of RAP in asphalt mixtures is widely recognized for enhancing sustainability while saving or improving pavement performance. Many studies have analyzed the effects of RAP content on mechanical properties, which proves not only its benefits but also its challenges. [PERSON] et al. tested the impact of foaming techniques and mixing temperature on foamed bitumen mixtures that contain RAP, showing that optimized foaming improves adhesion and workability [1, 2]. [PERSON] et al. found that high RAP content increases stiffness but may lead to cracking susceptibility [2], while [PERSON] et al. demonstrated that RAP incorporation of up to 100% is possible with warm mix technologies, ensuring adequate mechanical performance [3]. Although high RAP percentages improve stiffness and rutting resistance, they may reduce cracking resistance due to aged binder properties, which require a balance between RAP and FBC proportions. Foamed bitumen has emerged as a material in sustainable asphalt pavement recycling techniques, including cold in-place recycling (CIR) and full-depth reclamation (FDR). This is due to its ability to enhance adhesion and significantly reduce moisture sensitivity, which ensures the long-term durability of asphalt pavements, particularly in wet conditions. Previous studies have shown that foamed bitumen enhances the durability of cold-recycled asphalt mixtures by improving their resistance to environmental damage. In terms of evaluating the performance of foamed bitumen-stabilized mixtures, several advanced laboratory tests have been employed. The Hamburg Wheel-Tracking Test (HWTT) is widely used to assess rutting resistance, simulating the effects of long-term traffic loading [4,5]. In contrast, the indirect tensile strength (ITS) test offers valuable insights into the mixture's resistance to cracking and tensile strength, both of which are essential for ensuring the durability of pavements in colder climates [5]. Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized asphalt mixture design by enhancing the accuracy of performance predictions. Various AI techniques, such as support vector machines (SVM), random forest, and artificial neural networks (ANNs), have been applied to analyze the laboratory test results and optimize asphalt mixture compositions. Notably, SVM has been employed to predict the indirect tensile strength of foamed bitumen-stabilized base course materials, showing its effectiveness in improving model accuracy [6,7]. [PERSON] and [PERSON] employed Random Forest models to enhance asphalt mix design by capturing nonlinear interactions between mix components and pavement performance [8]. [PERSON] et al. used hyperparameter-optimized machine learning to model bitumen mixtures, showing improved prediction accuracy compared to traditional statistical methods [9]. Additionally, [PERSON] et al. developed a foaming quality control model using neural networks and parameter optimization, which significantly improved mixture stability and workability [10]. Building on these advancements, this study aims to identify the optimal RAP and FBC proportions to achieve a balance between rutting resistance and tensile strength. The proposed mixtures will be validated through HWTT and ITS tests under both wet and dry conditions, ensuring their performance across various environmental scenarios.