With the continued digitization of social and industrial processes, diverse and increasingly massive volumes of spatial-temporal data are being accumulated， which enable the use of data mining to fuel important applications. Thus, neural network-based techniques have been developed to capture spatial and temporal dependencies in spatial-temporal data. Most recently, the advances in generative techniques, including large language models, masked autoencoders, sequence-to-sequence models, diffusion models, and others, have led to their increased use in spatial-temporal data mining, thereby driving new advances in spatial-temporal data mining. This paper describes the general types of spatial-temporal data, all kinds of spatial-temporal data instances, and generative techniques, and it proposes a spatial-temporal data mining pipeline. Further, it delivers a structured overview of generative techniques for spatial-temporal data mining, grounded in a novel taxonomy. Moreover, by outlining promising research avenues enabled by generative techniques, the paper seeks to accelerate advances in spatial-temporal data mining.