## 1 Introduction  
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by distinctive social, behavioral, and communication challenges, with significant variability between individuals [1]. Traditional diagnostic methodologies, which primarily rely on observational and behavioral assessments, often face challenges related to accessibility, consistency, and timeliness. However, recent advancements in technology, particularly in artificial intelligence (AI), the Internet of Things (IoT), and sensor-based methodologies, present new possibilities for enhancing ASD detection. These tools are increasingly utilized to capture unique, data-driven markers of ASD, transforming diagnostic practices through more objective, precise, and scalable approaches [2, 3, 4]. The adoption of wearable sensors, eye-tracking systems, and multimodal VR environments holds the potential to provide an innovative means of observing ASD-related motor patterns, attentional processes, and physiological responses in real time. These technologies may enable continuous and non-intrusive monitoring, supporting early detection of ASD, and providing insights into behavioral and neurological traits that have been challenging to capture reliably. However, advances in current research is needed to validate their effectiveness. For instance, sensor-based methodologies could help identify stereotypical behaviors and motor patternsassociated with ASD in naturalistic settings, potentially providing data that might inform timely and tailored interventions [5; 6; 7]. Neuroimaging and microbiome analysis also extend this technological frontier by suggesting ASD-specific neurological and biological characteristics. AI-enhanced neuroimaging can help in identifying structural and functional brain connectivity patterns linked to ASD, contributing to a deeper understanding of its neuroanatomical basis [8; 9].  
This review examines the latest AI, IoT, and sensor-based methodologies in ASD research, highlighting their applications, benefits, and limitations. By synthesizing findings from diverse studies, our aim (and contribution to this research domain) is to demonstrate how these technological advancements are reshaping ASD diagnostics, offering a multidimensional perspective that facilitates more personalized, effective, and accessible support for people with ASD. Furthermore, building on our discussion of the identified challenges and open issues, we propose a comprehensive approach toward a holistic framework for ASD detection that combines emerging technologies with traditional diagnostic methods.  
The structure of this paper is as follows: Section 1 introduces the research context, highlighting the primary objectives and significance of the study. Section 2 provides background knowledge essential for understanding the research, including foundational concepts and relevant theoretical frameworks. Section 3 outlines the methodology, detailing the data collection and analytical techniques employed. Section 4 presents the results, emphasizing key findings. Section 5 discusses these findings in relation to the existing body of knowledge, identifying implications and limitations and offering a proposal for feature work. Finally, Section 6 concludes the paper, summarizing the contributions and suggesting directions for future research.