## 7 Conclusions  
ASD detection is a complex process that requires a significant amount of resources, including financial resources, time, and specialized expertise. The prompt detection of ASD may represent a significant difference in the future for individuals, as early intervention translates into a more promising outcome. Aiming to improve how ASD detection or classification is performed, researchers worldwide have been developing techniques involving machine learning (ML) and deep learning (DL) models and training them on respective datasets based on the requirements and modalities under consideration. These techniques can be very helpful in ASD detection, as they can accelerate the diagnostic process while maintaining the main aim of accuracy, efficiency, and correctness. In this document, we have explored various modalities for detecting or classifying Autism Spectrum Disorder (ASD), including questionnaires, facial images, MRI, EEG, eye tracking, and genetics, and how Artificial Intelligence (AI) can interact with these modalities to enhance their effectiveness. As part of the exploration, we have also studied various available datasets related to ASD research and discussed their use and limitations. Finally, we have carefully reviewed the limitations and research gaps in existing work, including the challenges and issues related to ASD detection or classification based on AI techniques. Overall, our review discusses the role of AI in the detection of ASD since it extensively describes various modalities and recent relevant work conducted by researchers in the ML and DL domains, while highlighting the limitations and research gaps that require attention and can pave the way for future research. In this study, we have also discussed popular ASD datasets from the literature to accelerate the development of behavioral and technical studies on ASD, and their download links were provided. The aim of this research was to highlight all the relevant aspects of ASD detection based on the research questions designed to extract extensive details on the topic. The main findings and recommendations are that numerous studies have introduced ML and DL techniques for detecting and classifying ASD. The area of ML and DL for ASD identification can make great progress by tackling these constraints and investigating the noted research gaps. This will open the door for creating more reliable, applicable, and practical models for diagnosing and treating ASD. One significant limitation identified in the reviewed literature is that most studies rely on a single modality for detecting ASD. Moreover, they are not leveraging the potential benefits of multimodalities, such as combining behavioral and biological indicators. AI can significantly assist in this process by integrating diverse data sources, including behavioral and biological indicators. By leveraging this approach, AI can enhance the diagnostic accuracy and timely detection of ASD. For example, we could consider a framework that combines fMRI and genetics with attention mechanisms to prioritize cross-modal features. Moreover, based on the reviewed studies, we observed that many researchers have used small and demographically imbalanced datasets. Such conditions can limit the robustness and generalization of AI models for ASD detection when applied to real-world populations. Therefore, future research should prioritize the collection of larger, more balanced, and ethnically diverse datasets, as these approaches will help make robust and generalizable inferences across different demographic groups in ASD detection models, thereby improving clinical dependability and reliability in various global settings. Finally, various AI models are typically evaluated in controlled and experimental conditions rather than validated in different clinical environments. Future work should evaluate these models in diverse clinical settings to assess their performance in various real-world scenarios.