This paper describes a sophisticated framework based on deep learning for diagnosing Autism Spectrum Disorder (ASD) through electroencephalogram (EEG) signals with an emphasis on real-time and mHealth solutions. The two heterogeneous EEG datasets BCIAUT-P300 (with ASD subjects) and SPIS Resting State (control subjects) were preprocessed and standardized to a common shape. For every sample, a compact and informative representation was constructed by extracting the mean and variance of eight EEG channels over 350 epochs. The classification of ASD cases versus control cases was performed using a lightweight CNN-LSTM hybrid architecture, which yielded a test accuracy of 94.74% and ROC AUC score of 0.90. Subsequently, post-training pruning was applied, resulting in over 40% reduction in model size, which allowed deployment via TFLite without any performance loss. The model was eventually embedded in an Android app, which allowed offline, on-device inference with average prediction times of 4–5 seconds per sample. This method achieves high classification accuracy while prioritizing temporal modeling and user-centered design. By combining the extraction of EEG statistical features with deep sequential models for mobile implementation, the system becomes a scalable, easy-to-interpret, and low-latency solution for autism spectrum disorder (ASD) screening that can be utilized in clinical as well as resource-limited environments.