This paper explores the integration of artificial intelligence (AI) and big data analytics (Big Data) into human decision support systems (DSS) to improve decision-making processes across different sectors. By leveraging predictive analytics, organizations can effectively analyze large data sets, predict future trends, optimize their operations, and make better-informed decisions. The study focuses on the theoretical underpinnings of AI, Big Data and DSS, exploring their interconnections and applications in practice. At the same time, it also addresses ethical issues such as algorithmic biases that can affect the outcomes of decision-making processes. In addition to the theoretical aspects, the paper performs a comparative analysis of three different optimization methods - Variance-Covariance Adaptive Sampling (VCAS), Adam and RMSProp - in order to understand their impact on the efficiency and stability of neural networks. This research provides a comprehensive framework for understanding the role of AI and Big Data in modern computing and offers valuable insights for future research and practical applications in various industries, including healthcare, agriculture, and smart cities.