The chapter discusses the challenges and advancements in the classification of Autism Spectrum Disorder (ASD) through hybrid attention-based learning of facial features. Traditional ASD screening methods are subjective and inconsistent, leading to delayed diagnoses. To address these issues, the research employs deep learning techniques, specifically transfer learning and facial recognition, to automatically extract and analyze facial features that may not be noticeable through visual inspection alone. The proposed methodology combines the strengths of fine-tuned ResNet101 and EfficientNetB3 architectures to enhance classification accuracy. The study involves meticulous dataset preprocessing, including augmentation and resizing, to standardize image dimensions. The hybrid attention learning model leverages self-attention techniques from natural language processing and sequence-to-sequence models to improve ASD prediction accuracy. The research aims for an accuracy rate of 96.50% and introduces significant innovations in the field of ASD classification, offering a non-intrusive, cost-effective, and scalable approach to early detection and intervention.The chapter discusses the challenges and advancements in the classification of Autism Spectrum Disorder (ASD) through hybrid attention-based learning of facial features. Traditional ASD screening methods are subjective and inconsistent, leading to delayed diagnoses. To address these issues, the research employs deep learning techniques, specifically transfer learning and facial recognition, to automatically extract and analyze facial features that may not be noticeable through visual inspection alone. The proposed methodology combines the strengths of fine-tuned ResNet101 and EfficientNetB3 architectures to enhance classification accuracy. The study involves meticulous dataset preprocessing, including augmentation and resizing, to standardize image dimensions. The hybrid attention learning model leverages self-attention techniques from natural language processing and sequence-to-sequence models to improve ASD prediction accuracy. The research aims for an accuracy rate of 96.50% and introduces significant innovations in the field of ASD classification, offering a non-intrusive, cost-effective, and scalable approach to early detection and intervention.