<p>Establishing reliable correlations among material properties across different scales is essential for enabling informed materials selection, performance estimation, and property screening. While most existing datasets and modeling efforts focus on predicting individual properties from composition or microstructure, limited attention has been given to uncovering the interrelations between modulus-related, thermal, and strength properties. Although empirical relationships have been proposed to relate certain properties, these are limited to specific alloys and fail to generalize across scales. To address the challenge, this paper leverages machine learning (ML) to uncover hidden nonlinear relationships between properties. Firstly, 1731 experimentally validated alloys were curated from the ANSYS GRANTA database, and materials properties were categorized into thermally, mechanically, and strength-related groups. Three ML models are employed to learn relationships among alloy properties: neural networks (NN), geometric harmonics (GH), and double diffusion maps (DDM). Results demonstrate that thermal and mechanical properties are strongly interrelated and can be predicted with high accuracy. However, the strength property is revealed to be difficult to model due to the missing information on post-processing treatments. To address this, we incorporated treatment metadata for iron-based alloys. The inclusion of these data led to an increase of over 70% in the average <i>R</i>², highlighting that explicitly accounting for post-processing information is necessary to make strength prediction from other macroscopic properties practically reliable.</p>