<p>Multiscale modeling has emerged as a powerful tool for analyzing the hierarchical architecture and performance of natural fiber-reinforced composites (NFRCs) across molecular, micro, meso, and macro scales. Given the growing demand for sustainable and biodegradable materials, natural fibers are increasingly replacing synthetic reinforcements. However, challenges such as poor fiber-matrix compatibility, environmental sensitivity, and modeling complexity still hinder their broader adoption. This review systematically examines state-of-the-art multiscale modeling approaches, including finite element analysis (FEA), molecular dynamics (MD), stochastic modeling, and machine learning (ML) integration, applied to NFRCs. Recent trends such as AI-assisted surrogate modeling and uncertainty quantification are highlighted. A critical synthesis of studies reveals that, while significant progress has been made in structural simulation and durability prediction, gaps persist in modeling moisture-induced degradation, standardizing representative volume element (RVE) methods, and analyzing long-term viscoelastic behavior. This review proposes a structured classification of modeling strategies and identifies emerging pathways that combine data-driven and physics-based approaches for designing high-performance, eco-friendly NFRCs.</p> Graphical Abstract <p></p>