eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-10-22 18 October Spl Edition 68543 article Mohit Dayal 1 Aparna Mahajan 2 Manju Khari 3 Department of Information Technology, Maharaja Agrasen University, Atal Shiksha Kunj , Distt. Solan, Himachal Pradesh, India. Department of Information Technology, Maharaja Agrasen Institute of Technology (MAIT), Maharaja Agrasen University, India. School of computer and System Sciences, Jawaharlal Nehru University, New Delhi, India. Mental illnesses are a major public health concern and one of the main causes of disability around the globe. They also provide substantial social and economic challenges. The non-invasive, real-time capabilities of electroencephalography (EEG) have made it a potential technique for identifying neural patterns linked to a range of mental diseases. A systematic assessment of EEG-based learning models for mental disease identification is presented in this article, underscoring the expanding use of AI and machine learning in neurodiagnostic. In addition to reviewing existing literature, we implement and evaluate three fine-tuned learning models (FTLM-1, FTLM-2, and FTLM-3) alongside three widely used transfer learning models (VGG19, ResNet50, and Mobile net). Comparative analysis reveals that the fine-tuned models achieved higher classification accuracy (up to 75%) compared to transfer learning models (up to 65%), underscoring the potential of task-specific model optimization in EEG-based mental health diagnostics. This work contributes to the advancement of intelligent, data-driven approaches for early and accurate detection of mental disorders. https://biomedpharmajournal.org/vol18octoberspledition/towards-intelligent-neurodiagnostic-a-systematic-assessment-of-eeg-based-learning-models-for-mental-illness-detection/ Electroencephalogram Fine Tuned Models Mental Illness Machine Learning Transfer Learning