Autism Spectrum Disorder (ASD) is a relatively rare condition between children; evaluations of the CDC's Autism and Developmental Disabilities Monitoring (ADDM) System suggest that approximately one in 36 kids is identified with ASD. Its symptoms are particularly prominent and observable in children aged 2–3 years. This review paper undertakes a broad analysis of ASD detection methods, harnessing the strength of both machine learning (ML) and neural network techniques. The review encompasses a systematic examination with a visual flowchart and a rigorous evaluation of various methodologies. These include traditional machine learning (ML) methods like Support Vector Machines (SVM) and K-nearest neighbors (KNN), as well as advanced neural network approaches like Artificial Neural Networks (ANN). Furthermore, the analysis presents a comparative table that assesses the performance of these methods in terms of their accuracy on diverse datasets. This multifaceted approach provides a holistic understanding and offers valuable insights for scholars and physicians in the field.