###### Abstract
reported that increasing foam volume fraction significantly improves thermal resistance, reducing thermal conductivity by more than 20%, thus enhancing the material's suitability for energy-efficient building envelopes. From an environmental perspective, [PERSON] et al. [7] also highlighted the green potential of foam concrete due to its low embodied energy, recyclability, and ability to incorporate industrial by-products. Furthermore, [PERSON] et al. [8] demonstrated that adding glass lightweight microspheres improves both the compressive strength and thermal performance of foam concrete, helping to mitigate the usual trade-offs between insulation efficiency and structural capacity. In addition, [PERSON] et al. [9] explored foam concrete's behavior in tunnel applications and confirmed its superior thermal buffering capacity, making it a viable material for underground environments where thermal regulation is critical. Collectively, these recent studies validate foam concrete as both a functional and sustainable alternative to traditional cementitious materials, supporting its strategic use in future resilient infrastructure. Although previous studies have discussed the thermo-mechanical properties of foam concrete, some significant gaps remain in quantifying the specific effect of individual material parameters on its overall performance. Compared with previous studies, this research offers several improvements. Earlier works [10; 11] focused mainly on predicting compressive strength using single machine learning models and limited datasets. In contrast, our study incorporates 170 data records extracted from publications and investigates multiple performance indicators, including compressive strength, flexural strength, and thermal conductivity. Moreover, we evaluate eight machine learning algorithms under the same conditions, allowing for a more comprehensive performance comparison. This approach helps bridge the gap between existing single-target models and practical multi-property prediction needs in engineering applications. The physical and chemical characteristics of foam concrete, such as density, porosity, and curing conditions, can lead to performance differences that limit its application [6; 7; 8]. Most current studies have primarily focused on the effect of individual parameters, while limited attention has been given to the comprehensive influence of multiple interacting properties. Thus, the scientific optimization to innovate the performance of foam concrete was a significant challenge [11]. Some scholars have assessed the influence of composition elements, such as cement, sand, mineral admixtures, and pore distribution on the compressive strength of foam concrete [12; 13; 14]. However, these studies have generally not incorporated dimensionless parameters, such as the water-to-cement ratio and sand-to-cement ratio. The dimensionless parameters can dramatically affect the strength of foam concrete. Furthermore, a wide range of thermal conductivity values for foam concrete [15; 16; 17] were reported in several studies. The difference likely results from different testing methods and materials used by researchers. Key factors affecting thermal conductivity involve the aggregates, mix ratios, and density [18]. While some machine learning models were established to estimate the heat transfer performance of foam concrete, many previous models failed to analyze the unique properties of foam concrete [19; 20]. The models often underestimated the influence of dimensionless parameters like the w/c and s/c ratios, which are crucial in predicting foam concrete's thermal conductivity [12; 21]. [PERSON] et al. used MLP networks with advanced imputation techniques to predict thermal conductivity, their focus was limited to a single target property and relied on relatively small and incomplete datasets. In contrast, our study adopts a broader multi-target modeling approach and leverages a more diverse dataset with complete parameter coverage and standardized preprocessing. [PERSON] et al.