While, many of the machine learning (ML) and artificial intelligence (AI) methods that are now commonly being used to answer questions across scientific disciplines have been around for some time, their widespread application to spatial data and spatially-explicit research questions is much more recent. The large number of excellent review papers and special issues in leading journals published in the last few years—which this issue of the Journal of Geographical Systems takes its place among—attest to the growing interest in the application and development of cutting-edge methodologies for spatial data. This editorial begins by proposing a new inclusive definition for spatial ML, then provides a brief overview of each of the six papers in this special issue, and ends with a suggestion of several possible directions for future research in spatial ML.