The Internet of Things (IoT) has emerged as one of the most transformative technologies of the 21st century, revolutionizing how industries operate and how devices interact within interconnected ecosystems. IoT enables billions of smart devices to collect, process, and share data, fostering unprecedented innovation across sectors like healthcare, manufacturing, smart cities, and transportation. However, the rapid expansion of IoT ecosystems has given rise to significant challenges in managing the vast volume, velocity, and variety of data generated by these devices. Traditional approaches to data management and processing often fall short, particularly in environments requiring real-time responsiveness, seamless scalability, and reliable decision-making.Integrating Artificial Intelligence (AI) with advanced data engineering techniques offers a powerful solution to these challenges. AI brings capabilities such as machine learning, predictive analytics, and intelligent decision-making, which, when combined with robust data engineering practices, enable efficient streaming data management. This integration supports real-time data processing, anomaly detection, predictive maintenance, and dynamic resource optimization, which are essential for creating intelligent IoT systems. By leveraging tools like real-time data pipelines, edge computing, and distributed architectures, AI-driven data engineering frameworks address critical issues, including data latency, resource constraints, and system scalability.This article delves into the intricate relationship between AI and data engineering within IoT ecosystems, focusing on streaming data management for smart devices. It explores the technical and theoretical underpinnings of integrating these fields, providing a comprehensive framework for optimizing IoT data streams. Key methodologies include employing machine learning algorithms to analyze real-time data, using edge computing to preprocess data closer to its source, and implementing scalable data pipelines for continuous processing.The findings of this study underscore the transformative potential of combining AI and data engineering in IoT ecosystems. Through experimental simulations and case studies, the research demonstrates how this integration enhances data flow efficiency, reduces latency, and improves the overall performance of IoT systems. For instance, in healthcare, AI-powered IoT devices enable real-time patient monitoring and predictive analytics, leading to improved medical outcomes. Similarly, in smart cities, integrated systems streamline traffic management, reduce energy consumption, and enhance public safety.This integration represents a paradigm shift in IoT ecosystems, laying the groundwork for intelligent, adaptive systems capable of meeting the demands of rapidly evolving industries. The study not only highlights the technological advancements enabled by this synergy but also identifies challenges such as integration complexity, resource limitations on edge devices, and the need for enhanced data privacy measures. Ultimately, this article serves as a blueprint for researchers, practitioners, and industry stakeholders aiming to unlock the full potential of IoT by bridging the gap between AI and data engineering.