Abstract

Big Data is a key driver that pushes Artificial Intelligence advancement by creating complex systems that promote automated operations while predicting outcomes and making wise decisions. Current data growth on structured and unstructured platforms requires the implementation of distributed processing systems, including cloud storage solutions and real-time data streaming. This paper examines the interconnected relationship between Big Data features and AI while analyzing fundamental approaches that handle extensive datasets during AI model creation, fine-tuning, and installation. This paper investigates how deep learning combines federated learning with reinforcement learning to manage big data environments. Our research includes three practical examples of how Big Data benefits healthcare through predictive analytics for disease detection and customized care, how automated vehicles use Big Data processing to achieve safety and efficiency, and how financial systems use AI processing of transactional data to detect fraud. The study indicates that Big Data enhances the functional capability of the AI model alongside performance, yet data privacy,
computational efficiency, and ethical factors remain essential problems. The research reveals modern trends and upcoming investigation paths that will define the upcoming generation of AI programs based on Big Data.

Keywords: Artificial intelligence, Big data, Case studies, Data-driven AI, Data processing, Deep learning, Machine learning