Real-time data analytics in edge gateways has become a crucial research area, focusing on enabling low-latency, high-throughput, and intelligent processing of data streams generated by connected devices before transmitting them to cloud infrastructures. Research papers in this domain explore frameworks and architectures for performing analytics directly at the edge gateway, reducing communication overhead, improving response times, and supporting applications such as autonomous vehicles, industrial IoT, smart healthcare, and smart cities. Studies investigate techniques for adaptive task scheduling, stream processing, workload balancing, and edge-based machine learning to provide predictive insights and anomaly detection in real time. Recent works also highlight security- and privacy-aware analytics frameworks, incorporating federated learning, encryption, and blockchain to safeguard sensitive data while maintaining performance. Additionally, multi-tier edge–fog–cloud integration is studied to optimize resource utilization, scalability, and reliability for latency-sensitive applications. Overall, real-time data analytics in edge gateways is a key enabler for intelligent, context-aware, and responsive edge computing ecosystems, bridging the gap between data generation and actionable insights.