This research provides a comprehensive and multifaceted examination of the convergence between Artificial Intelligence (AI), Big Data analytics, and autonomous multiagent systems. As the global digital ecosystem transitions toward ubiquitous connectivity, the requirement for robust theoretical frameworks to manage, interpret, and secure massive data streams has become paramount. This article investigates the foundational elements of agent-based systems, representation learning, and probabilistic machine learning, synthesizing their applications across diverse sectors including financial risk management, urban infrastructure, and healthcare scheduling. By strictly adhering to an interdisciplinary lens, the study explores how the integration of Internet of Things (IoT) in healthcare and fog computing improves Quality of Service (QoS) while addressing the inherent risks of networked finance. The research further delves into the nuances of sentiment analysis in Big Data and the application of visualization technologies for urban congestion management. A primary focus is placed on the evolution of predictive analytics from traditional statistical models to advanced neuro-dynamic programming and Markov decision processes. The article concludes by delineating the industry-wide shift toward intelligent cyber-physical systems, emphasizing the critical role of data quality and systematic supervision in mitigating the risks of the burgeoning Big Data era.

Artificial Intelligence, Big Data Analytics, Autonomous Agents, Financial Risk Modeling

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