Abstract: The present disclosure is directed to systems and processes for adaptively optimizing query execution. To improve efficiency and reduce costs of executing queries, machine learning may be utilized to automatically choose or adjust the processing blocks used to execute or process queries based on the nature of the cloud processing outputs and client preferences, among other considerations. A machine learning engine may group queries together into topics based on characteristics of the queries. The machine learning engine may receive execution statistics for the topics and, using the execution statistics, identify which topics enhance or optimize query execution. The machine learning engine may determine which topics may be executed by which processing blocks, at which times of day, and on which platforms, among other considerations in order to enhance or optimize cost or processing efficiency according to client-specified parameters.