With the growing application of artificial intelligence (AI) in mental health research, there is an increasing need to evaluate its clinical utility in diagnosing, predicting, and treating psychiatric disorders. This systematic review examines the role of AI methods in individuals undergoing neuroimaging assessments (fMRI, MRI, EEG). A comprehensive literature search was conducted in the PubMed Medline database and Cochrane Library, following PRISMA 2020 guidelines. The final analysis included 20 studies published between January 2020 and January 2025. The findings indicate that deep learning models, particularly convolutional neural networks (CNNs), demonstrate higher accuracy in classifying psychiatric conditions such as autism spectrum disorder (ASD) and schizophrenia compared to traditional machine learning approaches. Moreover, AI is increasingly being explored for predicting treatment response, particularly in depression and bipolar disorder, by integrating neuroimaging with behavioral and physiological data. Despite promising results, data heterogeneity, lack of standardized validation protocols, and ethical concerns remain significant challenges for clinical implementation. This review highlights the strengths and limitations of AI applications in psychiatric neuroimaging and underscores the need for standardized methodologies, larger datasets, and ethical guidelines to ensure the reliability and clinical applicability of AI-driven diagnostic tools. These findings provide a foundation for future research aimed at optimizing AI integration into psychiatric practice.