Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2442 2018 2018 CMR University (CMRU), Bangalore, India Monalin Pal CMR University (CMRU), Bangalore, India Rubini. P. Autism, a developmental and neurological disorder, impacts communication, interaction, and behavior, setting individuals with it apart from those without. This spectrum disorder affects various aspects of an individual's life, including social, cognitive, emotional, and physical health. Early detection and intervention are crucial for symptom reduction and facilitating learning and development. Recent advancements in machine learning and deep learning have facilitated the diagnosis of Autism by analyzing brain signals. This current study introduces an approach for Autism detection utilizing functional Magnetic Resonance Imaging (fMRI) data. The Autism Brain Imaging Data Exchange (ABIDE) dataset serves as the foundation, employing hierarchical graph pooling to abstract brain images into a graph structure. Graph Convolutional Networks are then used to learn node embeddings derived from sparse feature vectors. The model attains an accuracy of 87% on the 10-fold cross-validation dataset. This study proves to be cost-effective and efficient in identifying Autism through fMRI, making it suitable for near real-time applications. 2024 2024 89 96 10.54216/FPA.140207 https://www.americaspg.com/articleinfo/3/show/2442