###### Abstract
analyzed the individual or combined effects of a few admixtures on thermal and mechanical properties under fixed curing conditions; this study considers a broader range of material parameters and curing periods. The current approach uses data-driven modeling to explore non-linear relationships among multiple variables. [PERSON] et al. and [PERSON] et al. established a rigorous pipeline for HPC strength prediction using ensemble learning, RFE, and model interpretability tools; their approach focused exclusively on compressive strength. In contrast, the current study extends prediction to multiple properties--including thermal conductivity and flexural strength--based on a broader dataset and material parameter space. Moreover, our modeling strategy explores generalization behavior across different ML model types, rather than optimizing a single ensemble architecture. The complexity of thermal conductivity testing procedures makes it impractical to conduct experimental measurements for every concrete structure [22]. Thus, the data-driven model can be highly beneficial for it. Although machine learning (ML) has been widely used in multiple domains of concrete research, its use for predicting foam concrete properties, especially thermal conductivity, remains limited. This study focuses on using multiple material parameters to predict the Fc, Fr, and TC of foam concrete [22; 23]. Previous studies mainly focused on individual parameters, such as the water-to-cement ratio or density, using linear or empirical models [24; 25]. However, these methods often fail to capture the complex relationships between multiple factors. Some researchers have used machine learning to predict compressive strength [26; 27], but few studies have systematically analyzed both mechanical and thermal properties using a dataset. In this study, 170 experimental datasets were collected from the literature to explore the effects of multiple parameters, including W/C, FA/C, cement content, and Cag/Fag, using eight machine learning algorithms. Recent studies have demonstrated the growing use of machine learning (ML) techniques to predict the properties of foam concrete, particularly compressive strength and thermal conductivity. For example, [PERSON] et al. [28] applied Neural Networks, genetic algorithms, and ensemble models to estimate the compressive strength of lightweight foamed concrete. Using a dataset of 1030 samples, their best models achieved an R\({}^{2}\) of approximately 0.93, highlighting the potential of hybrid methods in enhancing prediction accuracy. Similarly, [PERSON] et al. [29] emphasized model transparency by integrating ex plainable artificial intelligence (XAI) with traditional ML techniques for concrete strength prediction, offering both accuracy and interpretability. In terms of thermal performance, [PERSON] et al. [30] implemented explainable ensemble learning models, such as XGBoost and CatBoost, on a dataset of 504 foam concrete samples. Their models reached R\({}^{2}\) values exceeding 0.98, showcasing state-of-the-art performance in predicting thermal conductivity. [PERSON] et al. [31] developed a Neural Network-based prediction method tailored for concrete thermal conductivity, achieving high precision across a range of compositions and conditions. In contrast, the current study expands upon these efforts by predicting three key performance indicators--compressive strength, flexural strength, and thermal conductivity--using eight machine learning algorithms (LMR, RR, SVR, NNR, DTR, RF, KNN, and GPR) applied to 170 validated experimental records compiled from 48 published sources. By incorporating a broader feature set--including W/C, FA/C, density, porosity, and the Cag/Fag ratio--and performing consistent 5-fold cross-validation, this work offers a more comprehensive and unified predictive framework. Notably, the NNR model achieved an R\({}^{2}\) of 0.987 for compressive strength prediction, outperforming many previous models.