Voice and language analysis using artificial intelligence has become a promising field in the diagnosis of mental disorders. This approach relies on analyzing speech patterns, tone of voice, speaking rate, and the linguistic content used by patients to detect psychological conditions such as depression, anxiety, and schizophrenia. Through deep learning techniques, artificial intelligence can process audio and linguistic data to identify subtle changes that may not be easily noticed by clinicians during traditional evaluations. Forexample, research has shown that individuals with depression often use words that reflect sadness or isolation, while patients with schizophrenia may exhibit changes in sentence structure and speech fluency. Moreover, machine learning techniques can be used to compare patient data with previous records, helping to provide accurate assessments of the progression of mental disorders and the likelihood of their worsening. These systems also enable continuous tracking of linguistic and vocal changes over time, offering a comprehensive view of a patient’s psychological condition. Despite significant advancements in this field, challenges remain regarding diagnostic accuracy, patient privacy protection, and the need to validate results through additional clinical examinations. Nevertheless, voice and language analysis using artificial intelligence holds great potential for improving the early detection of mental disorders, enabling healthcare professionals to deliver timely and appropriate treatments. As these technologies continue to evolve, artificial intelligence may become an effective tool not only for early detection of mental health conditions but also for monitoring patients over time and providing insights that could contribute to the development of new therapeutic approaches. Al-Mustaqbal University is the first one university in Iraq.