Autism Spectrum Disorder (ASD) is an intricate neurodevelopment disorder in which the expression of communication patterns and language patterns is deviated in comparison to normal development. Conventional diagnostic methods are quite intensive on judgmental clinical Newman in many cases, thus results may be slow and unreliable. The research presents a diagnostic model in the form of a transformer that is aimed at improving the accuracy and speed of ASD detection with the help of advanced Natural Language Processing (NLP) methods. The suggested system takes advantage of rich contextual and semantic representations of both transcript speech and text-based speech of ASD patients and controls based on a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) representation. The model can easily learn fine linguistic and pragmatic anomalies relevant to the language and communication in autistic speech by being trained on domain-specific corpora. Ignoring hyper parameters and performing experimental tests on benchmark datasets show that the fine-tuned BERT model drastically surpasses instead of heterogeneous machine learning models including Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks in terms of classification accuracy and F1-scores. Moreover, applications of interpretability with SHAP values and attention-based visualization identify essential linguistic characteristics that lead to ASD diagnosis to guarantee transparency and clinical usefulness. The study outlines the possibilities of transformer based linguistic modeling as a scalable, interpretable, and objective method of screening ASD at early levels.