With the continuous growth of spatio-temporal data, efficiently managing such data and extracting valuable information from it has become a crucial research topic. On one hand, the core of spatio-temporal data management (STDM) lies in the efficient storage, indexing, and querying of spatio-temporal data. However, traditional database technologies struggle to effectively handle characteristics of spatio-temporal data such as high spatio-temporal dynamics. Artificial intelligence (AI) techniques can effectively capture information such as the distribution characteristics of spatio-temporal data and query workloads, thus making spatio-temporal data management systems more intelligent. On the other hand, spatio-temporal AI (STAI) is devoted to applying techniques such as machine learning, deep learning, and reinforcement learning to spatio-temporal data analysis. STAI enables the automatic identification of patterns, trends, and associations in data and supports tasks such as spatio-temporal prediction, classification, clustering, and anomaly detection. However, across the entire process from data acquisition and model training to practical application, STAI faces challenges such as data heterogeneity, preparation complexity, and high barriers to use. These issues can be effectively mitigated by spatio-temporal data management technologies. Focusing on these topics, extensive research has been conducted in both industry and academia. This study first proposes a taxonomy of spatio-temporal data, categorizing it into independent data and associated data. Then, research progress on the synergy between STDM and STAI is systematically reviewed, and the research background and key techniques are summarized. Finally, commonly used datasets in this field are cataloged, representative applications are introduced, the main challenges are discussed, and future research directions are outlined.