  Autism Spectrum Disorder (ASD) represents a multifaceted neurodevelopmental
condition marked by difficulties in social interaction, communication
impediments, and repetitive behaviors. Despite progress in understanding ASD,
its diagnosis and treatment continue to pose significant challenges due to the
variability in symptomatology and the necessity for multidisciplinary care
approaches. This paper investigates the potential of Artificial Intelligence
(AI) to augment the capabilities of healthcare professionals and caregivers in
managing ASD. We have developed a sophisticated algorithm designed to analyze
facial and bodily expressions during daily activities of both autistic and
non-autistic children, leading to the development of a powerful deep
learning-based autism detection system. Our study demonstrated that AI models,
specifically the Xception and ResNet50V2 architectures, achieved high accuracy
in diagnosing Autism Spectrum Disorder (ASD). This research highlights the
transformative potential of AI in improving the diagnosis, treatment, and
comprehensive management of ASD. Our study revealed that AI models, notably the
Xception and ResNet50V2 architectures, demonstrated high accuracy in diagnosing
ASD.
