Autism Spectrum Disorder (ASD) Represents A Multifaceted Neuro-developmental State That Presents Significant Difficulties In Its Early Identification And Intervention. This Survey Explores The Recent Advancements And Methodologies In ASD Detection Leveraging Machine Learning (ML), Deep Learning (DL), And Neuroimaging Techniques. An Extensive Survey Of Literature Between 2018 And 2023 Reveals A Paradigm Shift In Diagnostic Approaches, Emphasizing The Integration Of ML Algorithms, Like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), And Decision-making Models, In Conjunction With Various Neuro-imaging Modalities Like Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), And Functional Near-Infrared Spectroscopy (fNIRS). These Modalities Facilitate The Identification Of Distinctive Biomarkers, Behavioral Patterns, And Neural Correlates Associated With ASD. The Survey Also Looks At Potential Ethical Issues, The Importance Of Early Detection Using ML-driven Methodologies, And The Changing Diagnostic Tool Landscape That Aims To Offer Timely And Individualized Interventions For People With ASD. The Combination Of These Data Demonstrates The Revolutionary Effect Of ML, DL, And Neuro-imaging In Improving The Accuracy Of ASD Detection, Allowing Access To Additional Potent Intervention Methods And A More Thorough Understanding Of The Neurobiology Underlying The Condition.
