Background <p>Mood and anxiety disorders, including major depressive disorder (MDD), bipolar disorder (BD), and anxiety disorders, affect millions worldwide and pose significant diagnostic challenges. Electroencephalography (EEG) has emerged as a non-invasive neurophysiological tool for identifying biomarkers of these disorders. However, the adoption of deep learning and machine learning in EEG analysis introduces interpretability concerns that limit clinical trust and applicability. Explainable Artificial Intelligence (XAI) offers transparent insights into model decisions, aiming to improve trust and clinical adoption of AI tools.</p> Objective <p>This systematic review aims to critically evaluate the landscape of EEG-based biomarkers for depression, anxiety, and bipolar disorder as informed by XAI techniques. Specifically, it explores the nature of EEG features selected through explainable models, their physiological plausibility, diagnostic performance, methodological quality, and clinical translatability.</p> Methods <p>Following PRISMA guidelines, a comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, Web of Science, and Embase databases for studies published between 2015 and 2025. Inclusion criteria were studies employing EEG-derived features for mood disorder classification using machine learning or deep learning, coupled with at least one post-hoc XAI method (e.g. SHAP, LIME, attention maps). A total of 145 studies were included. Thematic synthesis was organized into key domains: EEG protocol and feature extraction, model performance, XAI methods, interpretability outcomes, risk of bias, translational barriers, and publication trends.</p> Results <p>The review revealed that spectral features (e.g. alpha and beta power), functional connectivity metrics (e.g. PLI, PCC), and time-domain measures (e.g. Hjorth parameters) were the most commonly exploited EEG features. XAI-enhanced models consistently identified physiologically valid biomarkers such as frontal alpha asymmetry in MDD and frontal–parietal beta connectivity in BD. Machine learning models—including SVM, Random Forest, and deep neural networks—achieved high diagnostic accuracies (75–99%) across studies. SHAP and LIME were the most widely used interpretability methods, offering feature importance explanations that aligned well with known pathophysiological patterns. However, approximately 65% of studies exhibited high risk of bias due to small sample sizes, lack of external validation, and methodological inconsistencies. Ethical and translational concerns included limited clinician trust, lack of regulatory frameworks, and potential algorithmic bias. Publication trends showed exponential growth in EEG-XAI research post-2020, particularly in studies targeting depression.</p> Conclusion <p>EEG-XAI frameworks represent a transformative approach to neuropsychiatric diagnostics by coupling physiological insights with algorithmic transparency. While explainable models enhance diagnostic confidence and interpretability, their clinical utility is contingent upon methodological rigor, ethical safeguards, and cross-disciplinary validation. Future research should prioritize multi-site data harmonization, larger cohorts, and longitudinal designs to facilitate the safe and effective deployment of EEG-XAI tools in clinical psychiatry.</p> Clinical trial number <p>NA.</p>