Neuroanthropologists understand the dynamic enculturation of brain and cognitive functions in their external and internal environment. It necessitates the researchers to analyse big data for seeking proper understanding. Artificial intelligence (AI) is far better at predicting behavior than the human mind. The invention of anthromorphized robots which shows emotions and empathy like humans is also a result of AI applications. It holds great potential to advance diagnosis and treatment of patients with neurocognitive disorders. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. AI in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. Sociodemographic and other forms of population data offer rich information from large datasets because many countries collect population data regarding health, socioeconomic status, and social and family networks of older adults, such information may also provide an opportunity to compare outcomes across different countries and infer global health estimates of neurocognitive disorder burden. AI's strength lies in its ability to accommodate large quantities of multimodal data. Thus, AI can aid better understanding of unique factors and behaviors associated with cognitive decline that have been previously difficult to quantify. This study will offer the understanding of how AI made easier to understand and prevent neurocognitive disorders with better outcomes than before.