Computational methods encompass a wide range of techniques that include data-driven methods such as artificial intelligence (AI) and machine learning (ML) through to the more mathematical knowledge-driven mechanistic modelling. AI/ML techniques excel in processing large-scale datasets and can facilitate exploratory and predictive tasks such as clustering datasets, identifying biomarkers, and forecasting patient responses. Mechanistic models, on the other hand, provide a theoretical framework to simulate biological processes, validate hypotheses, and bridge knowledge gaps. The size and complexity of data generated to describe platelet biology, and indeed in all areas of the life sciences, is increasing. It brings with it a need to use a wider range of computational approaches to decipher, integrate, and explore this data. While AI/ML and mechanistic modelling are not yet commonplace in the arena of platelet biology, there are already many examples of how they can be useful. Here, we highlight current applications, explaining how and when they can be used. We explore how data-driven and knowledge-driven approaches can be combined to increase interpretability before discussing the limitations of these methods and possible future directions.