The increasing availability of diverse experimental and computational data has accelerated the application of deep learning (DL) techniques for predicting polymer properties. A literature review was conducted to show recent advances in DL applied to this field. For example, Li et al. (2023) achieved an \(R^2>0.96\) for predicting stiffness tensors of carbon fiber composites using a hybrid CNN–MLP model trained on microstructure images and two-point statistics. Aligning with this approach, Xue et al. (2023) compared DNN performance with genetic programming and minimax probability machine regression in predicting the lateral confinement coefficient for CFRP-wrapped RC columns, showing competitive predictive capability. These studies demonstrate that specialized architectures, including hybrid CNN–MLP models, feedforward ANNs, graph convolutional networks, and DNNs, provide high accuracy in predicting mechanical, thermal, and chemical properties of polymer composites and biodegradable plastics. Among these, DNNs have consistently shown superior performance in capturing complex nonlinear relationships within heterogeneous datasets, highlighting their suitability for materials characterization and optimization tasks. Building on these insights, this study investigates the effects of four natural fibers (flax, cotton, sisal, hemp) with densities around 1.48–1.54 g/cm \(^3\) , incorporated at 30 wt.% into three polymer matrices (PLA, PP, epoxy resin) with varying surface treatments (untreated, alkaline, silane). Samples were prepared via extrusion and injection molding (or casting for epoxy) under controlled processing conditions. Mechanical properties (tensile strength, modulus, elongation at break, impact toughness) were measured per ASTM standards, and density was determined by Archimedes’ method. Using 180 experimental samples, augmented up to 1500 using bootstrap technique, several regression models–linear, random forest, gradient boosting, DNNs–were developed to predict mechanical behavior. Best DNN model architecture (four hidden layers (128–64–32–16 neurons), ReLU activation, 20% dropout, a batch size of 64, and the AdamW optimizer with a learning rate of \(10^{-3}\) ) obtained through hyperparameter optimization using Optuna, delivered the best performance (R \(^2\) up to 0.89) and MAE reductions of 9–12% compared to gradient boosting, driven by the DNN’s ability to capture nonlinear synergies between fiber-matrix interactions, surface treatments, and processing parameters while aligning architectural complexity with multiscale material behavior.
