In this study, we calibrate a Telemac2D model, which solves shallow water equations, to improve tidal predictions. Observational tide gauge data is assimilated using the Particle Swarm Optimization (PSO) algorithm, to calibrate and estimate two critical model parameters: bottom friction and boundary conditions. The boundary conditions include the amplitude and phase of the predominant tidal constituents, while bottom friction is estimated as both constant and spatially varying. Our results show that while spatially varying bottom friction provides a slight improvement over constant friction, the most significant reduction in model error is achieved by calibrating both the amplitude and phase of the tidal constituents. The study concludes that the phase correction of predominant tidal constituents is the most critical parameter for improving the model's accuracy, emphasizing the importance of precise boundary condition calibration in tidal modeling.