Depression, a highly prevalent disorder affecting over 280 million people worldwide, is comorbid with many neurological disorders, particularly Alzheimer's disease (AD). Depression and AD share overlapping pathophysiology, and the search for accountable biological substrates made it an essential and intriguing field of research. The paper outlines the neurobiological pathways coinciding with depression and AD, including neurotrophin signalling, the hypothalamic-pituitary-adrenal axis (HPA), cellular apoptosis, neuroinflammation, and other aetiological factors. Understanding overlapping pathways is crucial in identifying common pathophysiological substrates that can be targeted for effective management of disease state. Antidepressants, particularly monoaminergic drugs (first-line therapy), are shown to have modest or no clinical benefits. Regardless of the ineffectiveness of conventional antidepressants, these drugs remain the mainstay for treating depressive symptoms in AD. To overcome the ineffectiveness of traditional pharmacological agents in treating comorbid conditions, a novel therapeutic class has been discussed in the paper. This includes neurotransmitter modulators, glutamatergic system modulators, mitochondrial modulators, antioxidant agents, HPA axis targeted therapy, inflammatory system targeted therapy, neurogenesis targeted therapy, repurposed anti-diabetic agents, and others. The primary clinical challenge is the development of therapeutic agents and the effective diagnosis of the comorbid condition for which no specific diagnosable scale is present. Hence, introducing Artificial Intelligence (AI) into the healthcare system is revolutionary. AI implemented with interdisciplinary strategies (neuroimaging, EEG, molecular biomarkers) bound to have accurate clinical interpretation of symptoms. Moreover, AI has the potential to forecast neurodegenerative and psychiatric illness much in advance before visible/observable clinical symptoms get precipitated.