<p>The omnipresence of microplastics (MPs) in aquatic environments poses a significant threat to ecosystems and human health. Previous literature has predominantly focused on the abundance, distribution, impacts, and detection methodologies of MP, with only limited studies addressing the classification and modelling approaches in MP research. Various modelling techniques have been developed to simulate the movement and dispersion of MPs; each has unique applications and methodologies. The review provides an overview of the various modelling approaches, highlighting their methodologies, strengths, and limitations. We attempt to study the existing categories of models: Lagrangian, Eulerian, Lagrangian-Eulerian (hybrid), mass-balance, statistical, and machine-learning models. Each category and its respective models are analysed thoroughly. In addition, this review highlights the significant challenges in current modelling practices, including data scarcity, unrealistic assumptions in biological processes, and the need for integrated modelling frameworks. For future research, we suggest incorporating biofouling, degradation rate, turbulence, and vertical mixing and conducting more in-situ experiments to obtain accurate input data and standardised sampling methods. This will enhance the reliability of models and their applicability in real-world conditions.</p> Graphical Abstract <p></p>