Simulation and computational modelling have become indispensable tools in aquaculture, enabling more informed decision-making across areas such as site selection, environmental interactions, management practices and daily farm operations. This chapter provides an overview of modelling techniques ranging from conceptual models to empirical and data-driven models, both deterministic and stochastic, and their applications within aquaculture. The chapter delves into the use of ocean circulation models, biogeochemical models, models at the farm and cage level, and data assimilation techniques to enhance model accuracy and predictive power. Key applications such as site selection, environmental impact assessment, water quality management, and carrying capacity estimation are examined in detail. Through detailed case studies, the chapter illustrates the implementation of hybrid epidemiological models, canopy flow simulations, and sea lice population and transport models. These examples highlight the capacity of modelling to capture complex interactions involving aquaculture systems and inform management decisions. The discussion highlights the growing importance of digital twins and real-time data integration in advancing sustainable and resilient aquaculture practices. By synthesising current methodologies and future directions, the chapter underscores the potential of modelling in addressing the complex challenges facing the aquaculture industry.