The increasing prevalence of neuropsychiatric disorders, including attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and autism spectrum disorder (ASD), has intensified the need for objective, scalable, and clinically actionable diagnostic frameworks. Electroencephalography (EEG), as a non-invasive and temporally precise neuroimaging modality, has emerged as a promising tool for identifying neurophysiological biomarkers associated with these conditions. Concurrently, advancements in predictive analytics and artificial intelligence have enabled the development of sophisticated models capable of uncovering latent patterns within complex biomedical datasets. This study proposes an integrated framework that combines EEG-based biomarker identification with machine learning-driven predictive modeling to enhance diagnostic precision, subtype classification, and treatment personalization in neuropsychiatric disorders. Drawing upon interdisciplinary literature spanning neuroscience, clinical psychiatry, and health informatics, the research explores the discriminative power of EEG features, the heterogeneity of disorder subtypes, and the challenges of model generalizability and validation. The methodology synthesizes quantitative EEG analysis, functional connectivity modeling, and predictive analytics approaches, emphasizing data pooling and heterogeneity assessment. Results highlight the potential of EEG-derived features in distinguishing disorder subtypes and predicting treatment outcomes, while also revealing limitations related to data variability and external validation. The discussion contextualizes these findings within broader technological and socio-economic transformations, including the role of automation and artificial intelligence in healthcare delivery. The study concludes by advocating for a multidimensional diagnostic paradigm that integrates neurophysiological data with advanced analytics, offering pathways toward precision psychiatry and improved clinical outcomes.