Autism Spectrum Disorder (ASD) stands as a complex and heterogeneous neurodevelopmental condition, encompassing a range of challenges in social interaction, communication, and behavior. Timely and accurate detection of ASD is crucial to initiate tailored interventions and support, optimizing developmental outcomes for affected individuals. This study delves into a novel avenue for ASD detection, leveraging the capabilities of Deep Convolutional Neural Networks (Deep CNN) applied to textual data. The model training and evaluation stages incorporate established metrics, assessing accuracy, precision, recall, and F1-score. The significance of this research extends beyond its technical prowess. By harnessing the power of deep-learning, this approach bridges the gap between cutting-edge technology and clinical practice. The implications of this study reverberate across multidisciplinary domains, from healthcare to machine learning, underscoring the potential for artificial intelligence to positively impact the lives of those affected by ASD.