Autism is a complex neurodevelopmental disorder. Difficulties in communication, social engagement, and repetitive behavior are hallmarks of ASD. Long-term results for people with ASD have been markedly improved by early diagnosis and care. In order to improve early intervention methods, this project aims to investigate the use of machine learning techniques to predict autism spectrum disorder (ASD) in early development. The review’s main goal is to summarize the condition of the field’s research at the moment and pinpoint its major trends, obstacles, and prospects. A thorough search of the literature on current developments in the field of predicting autism spectrum disorder (ASD) with machine learning approaches to improve early intervention tactics was done on PubMed, Google Scholar, and Scopus. The series of studies that have investigated the use of machine learning algorithms in the prediction of ASD are methodically examined in this review. The review’s conclusions highlight how machine learning might improve ASD early intervention efforts. This study advances our understanding of the techniques that have demonstrated promising outcomes in autism prediction by providing an overview and critical analysis of previous research.