Nowadays, with the rise in sensor technology, the amount of spatial and temporal data increases day by day. Fast, effective, and accurate analysis and prediction of collected data have become more essential than ever. Spatial Online Analytical Processing (SOLAP) emerged to perform data mining on spatial and temporal data that naturally contains the hierarchical structure used in many complex applications. In addition, uncertainty and fuzziness are inherently essential elements of data in many complex data applications, particularly in spatial-temporal database applications. In this study, FSOLAP is proposed as a new fuzzy SOLAP-based framework to compose the benefits of fuzzy logic and SOLAP concepts and is extended with inference capability to the framework to support predictive analytics. The predictive accuracy and resource utilization performance of FSOLAP are compared using real data with some well-known machine learning techniques such as Support Vector Machine, Random Forest, and Fuzzy Random Forest. The extensive experimental results show that the FSOLAP framework for the predictive analytics of various spatiotemporal events in big meteorological databases is considerably more accurate and scalable than using conventional machine learning techniques.