Mental health disorders represent a pressing global health challenge, demanding innovative approaches to detection and intervention. This study integrates multimodal AI techniques with EEG signal analysis to discern mental health states objectively. Emphasizing neurophysiological aspects, particularly EEG signal insights, we explore enhancements to improve accuracy through additional modalities such as ECG, SpO2, skin conductance, facial expression analysis and fNIRS. We evaluate emerging machine learning and deep learning algorithms alongside advanced feature extraction methods to develop predictive models that enhance detection accuracy. Methodological challenges are systematically addressed, including data pre-processing, feature selection, model validation, and AI model interpretability in clinical settings. We also discuss ethical considerations, such as data privacy and responsible AI deployment in mental health care. We present future research directions emphasizing large-scale validation studies, deployment of real-world AI assisted mental health tools, and integration of user feedback for continuous improvement. Our work presents the case of integrating multimodal AI with EEG signals, demonstrating potential and objective insights for mental health assessment and transforming early detection, personalized intervention, and patient outcomes.