Coastal shorelines play a significant role in shaping the future of seaside cities and towns due to coastline variations generated by large-scale human interventions, climate change, and sea-level rise. There are many challenges in forecasting coastal changes with the desired accuracy, given the epistemic and intrinsic uncertainties associated with this process. However, such uncertainties could be reduced through the data assimilation process which makes use of available observations data in enhancing the model reliability and robustness. The main focus of this research is to study the effect of adding the sequential data assimilation (DA) method, Ensemble Kalman Filter, on the free-form coastline evolution model, ShorelineS. ShorelineS can simulate different combinations of coastal features like the effect of hard structures and river inlets, and also can handle the splitting and joining of shorelines with distinctive sections. Consequently, experiments with a simple one-line model were first implemented for simplicity. Then DA was applied in ShorelineS afterwards. A quasi-transect approach was taken to solve the challenges of DA application in ShorelineS. Simulations’ outputs were sequentially transformed onto a set of transects created across the shoreline ensemble members. Afterwards, the intersection points between these transects and each ensemble member, as well as with the observed coastline, were used in the EnKF assimilation process. The joint model state (coastline positions) and model parameter (the longshore transport coefficient “B”) were successfully estimated, and several validation tests were carried out by both models. Using the simple one-line model: twin experiment and Pelnard-Considere based groin test. While the ShorelineS model: Longshore dispersion of a beach nourishment analytical test, and the well-monitored “Sand motor”, the mega-scale nourishment project in the Netherlands. Tests simulated using the simple one-line model showed the desired improvements to apply the application in the ...