Background: Natural fiber–reinforced polymer composites have emerged as compelling alternatives to conventional synthetic-fiber composites because of their favorable environmental profile, low density, renewability, and competitive mechanical properties in many applications (Rowell, Young & Rowell, 2002; Hu & Lim, 2007). Despite notable progress in processing and characterization, widespread adoption remains challenged by variability in fiber properties, interfacial compatibility, moisture sensitivity, and the need for robust predictive modelling frameworks (Hornsby, Hinrichson & Trivedi, 1997; Peng et al., 2018).

Objectives: This research-synthesis article aims to construct a detailed, publication-ready synthesis that integrates classical experimental studies of natural fiber composites with contemporary advances in characterization, hybridization strategies, compatibilization, and machine-learning-based property prediction. The objective is to provide a unified conceptual and methodological framework for researchers and engineers to design, process, and evaluate natural fiber composites for engineering applications.

Methods: The manuscript synthesizes experimental findings, fiber preparation techniques, composite processing routes, dynamic and static mechanical testing protocols, and analytical frameworks described across the provided literature. It constructs a methodical approach that aligns fiber extraction/pretreatment, matrix selection, interfacial modification, and comprehensive mechanical and thermal characterization, and situates recent machine-learning modelling approaches as complementary tools for optimization and uncertainty quantification (Mulenga, Rangappa & Siengchin, 2025; Liang et al., 2025).

Results: A convergent picture emerges: (1) alkali and coupling-agent treatments systematically enhance fiber–matrix adhesion and mechanical performance across many plant fibers (Yan et al., 2016; Yang et al., 2007); (2) hybridization with short glass or synthetic fibers improves strength and modulus while balancing cost and sustainability trade-offs (Reddy et al., 2018); (3) processing parameters, particularly fiber length distribution, dispersion, and composite microstructure, dominate composite performance and variability (Hornsby, Hinrichson & Trivedi, 1997; Harriette, Jorg & Martie, 2006); and (4) supervised machine-learning models, when trained on diverse, high-quality datasets, show strong promise in predicting flexural and tensile properties and directing experimental design (Hamzat et al., 2025; Mulenga, Ude & Vivekanandhan, 2021).

Conclusions: Natural fiber composites represent an adaptable, lower-carbon alternative to synthetic composites when designed with attention to fiber selection, surface chemistry, and microstructural control. Integrating careful experimental protocols with modern data-driven modelling can accelerate materials discovery and industrial translation, but standardized datasets, clear protocols for moisture conditioning, and deeper mechanistic models of interfacial physics are needed to reduce performance uncertainty.