This study explores the potential of leveraging ChatGPT to optimize depression intervention through explainable deep learning. The primary objective is to evaluate the viability of ChatGPT as a tool for aiding counselors in their interactions with patients, while also comparing its effectiveness to human-generated content (HGC). The research integrates state-of-the-art AI technologies, including ChatGPT, BERT, and SHAP, to enhance the accuracy and effectiveness of mental health interventions. ChatGPT generates responses to user inquiries, which are then classified using BERT to ensure reliability. SHAP is employed to provide insights into the underlying semantic constructs of the AI-generated recommendations, enhancing interpretability. The study found that the proposed methodology achieved an impressive accuracy rate of 93.76%. ChatGPT consistently uses a polite and considerate tone, avoiding complex or unconventional vocabulary and maintaining an impersonal demeanor. These findings highlight the potential significance of AIGC as a valuable complementary component in enhancing conventional intervention strategies. The study contributes by introducing a novel framework that integrates advanced AI technologies to enhance depression intervention measures. It prioritizes interpretability and transparency, providing valuable insights into the underlying reasoning behind AI-generated recommendations. Additionally, the study conducts a comprehensive linguistic analysis comparing AIGC and HGC, identifying distinguishing linguistic attributes and contributing to a deeper understanding of the linguistic properties of AI-generated recommendations. The results of the study demonstrate the superior performance of Roberta's deep learning model compared to baseline models, achieving enhanced accuracy and efficiency in classification tasks. The integration of XAI techniques, particularly SHAP, enhances the interpretability and transparency of the deep learning model, making it more accessible and understandable for healthcare professionals. Overall, the study highlights the promise of using large language models in healthcare, particularly in advancing sophisticated healthcare systems capable of augmenting patient care and counseling practices.This study explores the potential of leveraging ChatGPT to optimize depression intervention through explainable deep learning. The primary objective is to evaluate the viability of ChatGPT as a tool for aiding counselors in their interactions with patients, while also comparing its effectiveness to human-generated content (HGC). The research integrates state-of-the-art AI technologies, including ChatGPT, BERT, and SHAP, to enhance the accuracy and effectiveness of mental health interventions. ChatGPT generates responses to user inquiries, which are then classified using BERT to ensure reliability. SHAP is employed to provide insights into the underlying semantic constructs of the AI-generated recommendations, enhancing interpretability. The study found that the proposed methodology achieved an impressive accuracy rate of 93.76%. ChatGPT consistently uses a polite and considerate tone, avoiding complex or unconventional vocabulary and maintaining an impersonal demeanor. These findings highlight the potential significance of AIGC as a valuable complementary component in enhancing conventional intervention strategies. The study contributes by introducing a novel framework that integrates advanced AI technologies to enhance depression intervention measures. It prioritizes interpretability and transparency, providing valuable insights into the underlying reasoning behind AI-generated recommendations. Additionally, the study conducts a comprehensive linguistic analysis comparing AIGC and HGC, identifying distinguishing linguistic attributes and contributing to a deeper understanding of the linguistic properties of AI-generated recommendations. The results of the study demonstrate the superior performance of Roberta's deep learning model compared to baseline models, achieving enhanced accuracy and efficiency in classification tasks. The integration of XAI techniques, particularly SHAP, enhances the interpretability and transparency of the deep learning model, making it more accessible and understandable for healthcare professionals. Overall, the study highlights the promise of using large language models in healthcare, particularly in advancing sophisticated healthcare systems capable of augmenting patient care and counseling practices.