Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this studyistoexamineandprovideacomprehensivestate-ofthe-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b)functionalMRIand(c)hybridimagingtechniquesover the past decade. The accuracy of the studies with a large numberofparticipantsisingenerallowerthanthosewith fewer participants leading to the conclusion that further large-scalestudiesareneeded.Anexaminationoftheage of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute signiﬁcantly to the early and rapid diagnosis of ASD in the coming years andbecomeavailabletocliniciansinthenearfuture.This review is aimed to facilitate that.