• Siddharth Anand Sancheti
• Aakash Kishor Kale
• Siddhesh Navnath Sabale
• Aditya Salish
• Shubhangi Chavan

Artificial intelligence and machine learning have the power to revolutionize the field of mental health recognition. By analyzing large sets of data. Al-based systems can detect patterns and make predictions that can help diagnose and treat mental health issues. Al and machine learning used in mental health recognition is a great success. For example, machine learning algorithms have been used to predict suicide risk by analyzing social media data, and natural language processing techniques have been used to analyze speech patterns and detect depression. In addition to recognizing mental health issues. Al can also be used to develop more effective treatment options. By analyzing large sets of data, Al can identify patterns in patient behavior and tailor treatment plans to individual needs. This could potentially reduce the need for trial and-e for approaches and lead to more successful treatment outcomes. However, it's important to note that Al is not a replacement for human expertise and care. While Al can assist healthcare professionals in making more informed decisions, it should not be relied upon as the sole basis for diagnosis or treatment. Additionally, ethical considerations must be taken into account when developing and deploying AI systems in the mental health domain.

Artificial intelligence, Machine Learning, Mental Health, Treatment.

"Mental Health Monitoring using Facial Recognition", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l517-l523, April-2024, Available :http://www.jetir.org/papers/JETIR2404B68.pdf

"Mental Health Monitoring using Facial Recognition", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl517-l523, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B68.pdf