<p>Polyurethane (PU) is a highly popular polymer used in several industrial spheres, the thermal and mechanical functioning of which is determined most important by its melting temperature (Tm). Experimentally, determination of melting temperature (Tm) may be time consuming and resource consuming. To counter this, molecular descriptors are applied in a quantitative structure -property relationship (QSPR) system to forecast Tm over a large range of structurally diverse PU materials. The computational methodology underlines the usefulness of the descriptor-based modeling system to polymer science because it allows one to assess thermal behavior in a fast and inexpensive manner. A set of PU structures is then chosen with a range of molecular descriptors of topological, structural, and physicochemical descriptors being calculated. In order to meet high modeling and statistical consistency, the preprocessing of the data of the descriptors is performed through such techniques as centering, normalization of the variances, standardization, and min-max scaling. A number of regression and machine-learning techniques, such as Ordinary Least Squares (OLS), Partial Least Squares (PLS), K-Nearest Neighbors (KNN), and the Support Vector Machines (SVM) are subsequently used to obtain predictive models of Tm. The standard metrics of accuracy are used to measure the predictive reliability of model performance, which proves that molecular descriptors combined with suitable preprocessing and modeling strategies can allow accurate and efficient prediction of Tm and provide a scalable methodology to other polymeric systems that reduces the reliance on experimental studies.</p>