Major Depressive Disorder (MDD) is a psychiatric disorder diagnosed globally and is a contributor of disability due to its complex nature and varied treatment response. Current complexities to treating MDD (e.g., pharmacological or psychotherapy) encompass delayed or ineffective treatments, resulting in the development of treatment-resistant MDD. Advancements in Emerging Technologies (e.g., neuroimaging, genomic research, and artificial intelligence) enhance treatment approaches of MDD through predictive modeling to optimize treatments. Neuroimaging and genetic-based biomarkers combined with AI-driven biomedical analytical frameworks are promising predictive treatment responses to optimize psychiatry. Neuroimaging tools such as fMRI, PET and EEG may pinpoint depression pathology and neurobiological components as well as structural-and functionally-related deficits across different brain systems (e.g., prefrontal, amygdala, and hippocampus). Genetic variants and data have the propensity to identify polygenic risk factors, pharmacogenomic markers, and epigenetic mechanisms that underlie the unique susceptibility for MDD, including the possibility of distinguishing psychosis from depression by analyzing early structural brain changes with neuroimaging. While integrating biomarkers with AI machine learning and deep learning techniques produce multimodal data from neuroimaging, genetics, and clinical assessments, there remain obstacles with limited datasets, interpretability, privacy of information, and algorithm bias. Translating AI-based predictive models into clinical work ultimately may yield grounded treatment effectiveness, reduce treatment trial-and-error, and revolutionize biological, neurological, and psychological biometrics to forecast treatment responses and ameliorate MDD.