Autism Spectrum Disorder (ASD or Autism) is a neurodevelopmental disorder that is characterized by challenges in social communication skills and the presence of restricted and repetitive behaviors during early childhood. ASD poses a significant public health challenge with increasing prevalence rates worldwide. Early diagnosis and intervention are critical for improving outcomes in children with ASD. However, current diagnostic methods often involve subjective assessments and are time-consuming. Currently, there are no known biomarkers for ASD, and the diagnosis is based on phenotypic manifestations observed by trained clinicians over time. Additionally, the heterogeneity of Autism and associated co-occurring conditions pose further challenges for screening and early detection. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) are transforming ASD screening and diagnosis. These computational technologies are capable of analyzing complex datasets and multiple modalities, including multi-omics, brain images, behavior assessments, medical and background information, and registry data to identify patterns that may not be evident to clinicians or parents. This article reviews recent developments in the application of AI/ML for ASD screening and early diagnosis. It also covers the use of AI/ML in understanding the biological underpinnings of ASD.