Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by challenges in social interaction, communication, and repetitive behaviors. Early-life brain development is linked to autism spectrum disorder (ASD), which has an impact on a person’s social interactions and interaction problems (Mahedy Hasan, et al., “A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders” 11, Digital Object Identifier, https://doi.org/10.1109/ACCESS.2022.3232490 ). Despite significant progress in ASD research, several gaps remain in understanding the disorder and its effective management. This survey paper aims to provide a comprehensive overview of the recent advancements and applications of machine learning (ML) techniques in ASD research while identifying current research gaps and outlining future directions. The survey explores various domains where ML has demonstrated promising potential, including early detection and diagnosis, prediction of treatment outcomes, identification of biomarkers, and behavioral analysis. Categorizing existing studies based on the type of ML algorithms employed, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning, provides a structured understanding of the field. Furthermore, this survey delves into the diverse range of datasets used in ASD research, including clinical assessments, observational data, neuroimaging, genetic data, and wearable devices. This survey discusses the challenges associated with data collection, preprocessing, feature extraction, and interpretation, highlighting the need for standardized protocols and collaborations to ensure the reproducibility and comparability of results. Ethical considerations and limitations of ML applications in the ASD domain are also discussed, addressing concerns such as privacy, algorithmic bias, and the importance of interdisciplinary collaborations between clinicians, researchers, and data scientists. Additionally, this survey identifies potential future directions for machine learning in ASD research, such as the integration of multimodal data, the explainability of ML models, and the development of user-friendly tools for clinicians.