Given the high risk of progression and chronicity of psychological disorders, as well as severe outcomes such as substance dependence and suicide, early and comprehensive assessment of these conditions is of critical importance. Artificial intelligence (AI)-assisted methods offer substantial potential over traditional approaches owing to their speed, accuracy, and capacity for multimodal data processing. Advances in machine learning and deep learning enable the integration of physiological and behavioral data collected from everyday devices such as smartphones and wearables into clinical processes, facilitating real-time monitoring and personalized interventions. Multimodal data—including facial expressions, postural indicators, social media content, linguistic patterns, and biomarkers—can be leveraged for early detection of mood disorders, identification of risk periods, and dynamic tracking of treatment response. Conversational agents and EEG biomarkers further support the diagnosis of conditions such as depression and bipolar disorder and the objective monitoring of symptoms through AI-based algorithms. Nevertheless, these methods are susceptible to cultural, individual, and contextual variability, data quality issues, and user motivation, which may lead to erroneous clinical inferences. Breaches of privacy and data security pose additional ethical and legal risks. Accordingly, AI-based systems should be designed to support rather than replace clinical decision-making; findings must be interpreted with caution and triangulated with other data sources. Multidisciplinary research is needed to refine these tools and address their limitations. When appropriately implemented, AI-assisted assessment instruments can complement traditional clinical practices, enhancing screening, preliminary evaluation, and risk analysis, and enabling mental health professionals to develop more comprehensive and reliable case formulations.