<p>Polyvinyl alcohol (PVA) is a widely used polymer in many applications. The nanoparticles’ incorporation into the polyvinyl alcohol structure can significantly modify the electrical characteristics by benefiting the properties of both polymer and nanofillers. Although the dielectric constant of PVA-based nanocomposites has been comprehensively measured, no general predictive model currently exists for its estimation. So, this study develops a robust, data-driven framework to predict the dielectric constant of PVA nanocomposites as a function of nanofiller type, filler concentration, temperature, and frequency. First, a comprehensive dataset of 1698 experimental measurements was gathered from the literature, covering nanofillers including CuO, TiO₂, ZnO, Al₂O₃, GO, V₂O₅, SrTiO₃, and PbO. Feature relevance was first analyzed using multiple linear regression, followed by the implementation and comparison of six machine learning models, i.e., categorical boosting, gradient boosting (GradBoost), extreme gradient boosting, least-squares support vector regression, multilayer perceptron neural network, and adaptive neuro-fuzzy inference system. Among these, the GradBoost model demonstrated superior predictive performance and generalization capability. The GradBoost model achieved high accuracy in cross-validation (1359 samples) with AARD = 3.38%, RMSE = 3.49, MAE = 0.83, and R = 0.99926, and maintained strong performance on the external test set (339 samples) with AARD = 9.32%, RMSE = 4.24, MAE = 1.76, and R = 0.99877. The proposed model represents the first generalized predictive tool for estimating the dielectric constant of PVA-based nanocomposites across multiple nanofillers and operating conditions. This approach provides a practical and accurate alternative to experimental study, enabling accelerated design and optimization of polymer nanocomposites for electronic and dielectric applications.</p>