Synthetic biology is an interdisciplinary field that integrates genetic programming, protein engineering, computer-aided biological modeling, cell state analysis, and artificial life. The convergence of computational tools like bioinformatics and data science in synthetic biology is not only supportive but also integral to overcome the inherent complexity of biological systems. These tools also contribute to access the full potential of synthetic biology for a wide range of applications in medicine, energy, manufacturing, and environment. This combination has revolutionized synthetic biology, opening up new opportunities in the engineering of cells, protein functions, and metabolic pathways, and providing an invaluable resource for synthetic biology. The application of artificial intelligence (AI) and machine learning (ML) techniques, especially deep learning (DL), is becoming more and more popular and widespread for various purposes. It provides new approaches to model core biological processes and integrates different types of omics data, which can help predict how these processes are disrupted in disease. This chapter will provide a short introduction about the history and applications of bioinformatics and data sciences, especially in the field of synthetic biology. Additionally, the advantages and challenges of developing artificial intelligence and machine learning models to obtain high accuracy and reliability will also be analyzed. Common computational and data science tools will be summarized to provide a big picture for the current status and developmental potential of informatics technology and data science in the synthetic biology field.