The characteristics of autism spectrum disorder include repetitive habits, fixed interests, and persistent difficulties with social interaction and communication. ASD is a neurological condition. For those with autism spectrum disorder (ASD), early detection is important for early intervention and better outcomes. Many ML models, such as SVM for classification, Random Forest-CART (Classification and Regression Trees), Random Forest-Id3 (Iterative Dichotomiser3), Linear Discriminant Analysis (LDA) for feature extraction, and many more, have been introduced to diagnose ASD. This study includes a brief summary of the studies on machine learning detection of ASD. After gathering more than 70 research publications, 50 were ultimately chosen for this review. The primary goal of this review is to identify the most recent developments in machine learning for the identification of ASD and to pique the interest of upcoming researchers in this area.