<p>This study systematically explores the intersection of big data and federated learning, focusing on their transformative applications and collaborative potential across diverse domains. By analyzing state-of-the-art studies, the review examines innovative use cases in industries such as healthcare, finance, IoT, smart cities, and edge computing. Through qualitative and quantitative analysis, the study highlights federated learning’s ability to leverage extensive datasets for improved decision-making and operational efficiency while ensuring data privacy and security. The research provides a comparative evaluation of current advancements, addressing key challenges such as scalability, data heterogeneity, and implementation barriers. It also identifies innovative strategies to overcome these limitations, paving the way for more robust and scalable federated learning systems. By offering a unique perspective on the evolving field of privacy-preserving machine learning, this work serves as a valuable reference for researchers and practitioners. It emphasizes the critical role of federated learning in advancing secure, efficient, and collaborative AI-driven applications in the modern era of data privacy and machine learning research.</p>