Mood disorders exhibit variations in severity, symptoms, and treatment response, highlighting the need for personalized psychiatry. The integration of patient-specific biomarkers into treatment selection holds the potential to significantly advance this field. Machine learning is increasingly being embraced in healthcare, further emphasizing its role in this context. After training, the patient is the party, as they may analyze an individual patient rather than an entire group. In recent times, deep learning, which is a specialized domain within machine learning, has gained significant popularity owing to its capability to effectively leverage voluminous neurosurgical data and incorporate non-imaging biomarkers. The fundamental principle underlying deep learning revolves around the utilization of neural networks: there are multiple hidden layers, levels of abstraction increase, employed to acquire hierarchical representations of data. This is evidenced by the application of deep learning techniques. Although the results of deep learning algorithms are difficult to interpret, it holds great promise in the field of psychiatry, is widely regarded as one of the most promising approaches in the field of machine learning and is often criticized as a "black box" model.