This paper proposes a hybrid attention-based learning model to enhance the classification of Autism Spectrum Disorder (ASD) by leveraging deep learning techniques. The study addresses the challenges of traditional ASD screening, including subjectivity, complexity of facial analysis, and the need for high precision and recall in distinguishing autistic and normal faces. The proposed methodology combines the strengths of fine-tuned ResNet101 and EfficientNetB3 architectures to improve classification accuracy. The research involves meticulous dataset preprocessing, including augmentation and resizing to standardize image dimensions. The hybrid attention learning model integrates transfer learning and deep learning techniques, which are fine-tuned using ResNet101 and EfficientNetB3. The experimental process begins with comprehensive dataset preprocessing, encompassing augmentation and image resizing to ensure uniform dimensions. EfficientNetB3 and ResNet101 models, when combined, create a potent synergy that balances computational efficiency with robust feature extraction, enhancing their versatility and effectiveness across diverse machine learning tasks. A thorough performance analysis is conducted to evaluate the accuracy of ASD prediction, aiming for an accuracy rate of 96.50%. Notably, the research introduces self-attention techniques from natural language processing and sequence-to-sequence models to advance ASD prediction, marking a significant innovation in this field. The study also explores the effectiveness of transfer learning techniques coupled with hyperparameter tuning in classifying medical images, focusing on differentiating ASD and control cases. The findings of this investigation offer valuable insights for researchers and developers assessing the utility of diverse transfer learning methodologies within the medical image analysis. The contributions of this study include the development of an innovative hybrid methodology, the creation of an improved hybrid model for precise Autism identification, the establishment of ASD dataset classification, and the demonstration of superior performance relative to regularized methods and traditional CNN architectures. These contributions collectively advance the field of ASD classification and offer promising prospects for improved diagnostic accuracy.This paper proposes a hybrid attention-based learning model to enhance the classification of Autism Spectrum Disorder (ASD) by leveraging deep learning techniques. The study addresses the challenges of traditional ASD screening, including subjectivity, complexity of facial analysis, and the need for high precision and recall in distinguishing autistic and normal faces. The proposed methodology combines the strengths of fine-tuned ResNet101 and EfficientNetB3 architectures to improve classification accuracy. The research involves meticulous dataset preprocessing, including augmentation and resizing to standardize image dimensions. The hybrid attention learning model integrates transfer learning and deep learning techniques, which are fine-tuned using ResNet101 and EfficientNetB3. The experimental process begins with comprehensive dataset preprocessing, encompassing augmentation and image resizing to ensure uniform dimensions. EfficientNetB3 and ResNet101 models, when combined, create a potent synergy that balances computational efficiency with robust feature extraction, enhancing their versatility and effectiveness across diverse machine learning tasks. A thorough performance analysis is conducted to evaluate the accuracy of ASD prediction, aiming for an accuracy rate of 96.50%. Notably, the research introduces self-attention techniques from natural language processing and sequence-to-sequence models to advance ASD prediction, marking a significant innovation in this field. The study also explores the effectiveness of transfer learning techniques coupled with hyperparameter tuning in classifying medical images, focusing on differentiating ASD and control cases. The findings of this investigation offer valuable insights for researchers and developers assessing the utility of diverse transfer learning methodologies within the medical image analysis. The contributions of this study include the development of an innovative hybrid methodology, the creation of an improved hybrid model for precise Autism identification, the establishment of ASD dataset classification, and the demonstration of superior performance relative to regularized methods and traditional CNN architectures. These contributions collectively advance the field of ASD classification and offer promising prospects for improved diagnostic accuracy.