Living systems are extremely sophisticated and difficult to accurately describe and predict, posing challenges in designing synthetic biological systems. Therefore, massively parallel trial-and-error processes are often required to optimize synthetic biological systems. In recent years, intelligent technology has experienced rapid development and has demonstrated continual learning capacity from massive data and intelligent exploring ability in unknown space, which perfectly meets the needs of the current trial-and error platform of synthetic biology engineering and shows great potential in mining complex biological patterns and in designing biosystems. This article reviews the progresses of applying artificial intelligence (AI) in the fields of synthetic biological parts engineering, circuit engineering, metabolic engineering, and genome engineering. This article also analyzes a series of challenges in data standardization, platform intellectualization, experimental automation, and accurate prediction of cross-over studies between AI and synthetic biology. By solving these challenges, the entire workflow of “design-build-test-learn” in synthetic biology is expected to be revolutionized by AI, and creating an “AI synthetic biologist” would in turn lead to the technological advances in AI.