Designing and analyzing materials with desired properties is a complex process involving experiments and conducting highly rigorous efforts, which can take significant time to achieve success. In order to achieve the desired properties, scientists and researchers must go through a series of experiments to understand the materials’ behavior. This involves testing the materials under different conditions and analyzing the results. Once the properties of the material have been identified, researchers can work on designing the material to meet specific requirements. This process can be time-consuming and requires significant effort, but ensuring the material meets the desired specifications for its intended use is necessary. In the recent past, an increase in the availability of experimental data and computational resources has led to the development of a new emerging field called material informatics. This field uses the computational approach for designing new molecules, optimizing an existing material’s molecular structure to achieve some desired properties, and predicting a new material’s properties even before their synthesis. This computation-based approach not only helps to reduce time but also helps to reduce the waste of resources and money involved in the experimental procedure. Recently, a new method called quantitative read-across structure–property relationship (q-RASPR)Quantitative Read-Across Structure-Property Relationship (q-RASPR) was developed by Banerjee and Roy to predict various properties associated with the materials. The q-RASPR method is an integrated technique developed by the fusion of statistical quantitative structure–property relationship (QSPR)Quantitative Structure-Property Relationship (QSPR) and similarity-basedSimilarity-based method read-across (RA)Read-Across (RA) methodologies, and hence helps in data gap fillingData gap filling by eliminating the disadvantages of both QSPR and RA approaches. The main advantage of a q-RASPR model is that it contains information on both similarities as well as structural and physicochemical characteristics of the molecules. To date, q-RASPR has been used in various fields of material science, for example, to predict the effective surface area of perovskitesEffective surface area of perovskites, thePerovskites power conversion efficiencyPower conversion efficiency of different classes of dye-sensitized solar cells (DSSCs)Dye-Sensitized Solar Cells (DSSCs), certain stability and performance parameters of energetic materialsEnergetic materials, the adsorption capacityAdsorption capacity of different kinds of microplasticsMicroplastics in diverse water environments, and reorganization energyReorganization energy of p-type organic semiconductors (OSCs). The developed q-RASPR models’ predictive results showed the method's quality, reliability, and effectiveness. The q-RASPR models are reproducible with high predictive power and ease of interpretability. The q-RASPR method can be considered a promising approach for the prediction of interested property in the field of material science as it is efficient in estimating different properties for smaller and larger data sets as well.