Autism spectrum disorder (ASD) is a neurological issue that impacts brain function at an earlier stage. The autistic person realizes several complexities in communication or social interaction. ASD detection from face images is complicated in the field of computer vision. In this paper, a hybrid GEfficient-Net with a Gray-Wolf (GWO) optimization algorithm for detecting ASD from facial images is proposed. The proposed approach combines the advantages of both EfficientNet and GoogleNet. Initially, the face image from the dataset is pre-processed, and the facial features are extracted with the VGG-16 feature extraction technique. It extracts the most discriminative features by learning the representation of each network layer. The hyperparameters of GoogleNet are optimally selected with the GWO algorithm. The proposed approach is uniformly scaled in all directions to enhance performance. The proposed approach is implemented with the Autistic children’s face image dataset, and the performance is computed in terms of accuracy, sensitivity, specificity, G-mean, etc. Moreover, the proposed approach improves the accuracy to 0.9654 and minimizes the error rate to 0.0512. The experimental outcomes demonstrate the proposed ASD diagnosis has achieved better performance.