Abstract
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that affects about 1 in 100 children worldwide. Early detection supports timely intervention, but many families still face long waits and uneven access to specialists. In low-resource settings, diagnostic delays can reach around 24 months. In this paper, we synthesize technology-enabled approaches for ASD detection across four data modalities. These include facial expression and gaze analysis, voice and audio assessment, sensor and brain-imaging signals, and multimodal integration. We summarize the main methods and report performance ranges from 68.5% to 98.9% accuracy across studies. We find that deep learning approaches often perform best, especially when they use transfer learning and multimodal fusion. Compared with prior reviews that focus on a single modality or emphasize algorithms alone, we connect performance evidence to clinical readiness factors such as cost, accessibility, scalability, validation, and real-world deployment constraints. At the same time, key barriers remain. Common issues include limited dataset diversity, weak cross-cultural generalizability, a lack of clinical validation, and ethical risks in real-world deployment. We close by outlining practical directions for building detection tools that are clinically useful and globally accessible.