The rapid growth of real-time applications demands a robust, high-performance event-streaming architecture capable of handling massive, continuous, and heterogeneous data flows. Conventional streaming models were significantly less scalable and more prone to failure due to their limited fault tolerance and latency bottlenecks, especially under unpredictable conditions. The problems and issues faced by conventional models are addressed by introducing a Multi-Agent AI-Driven (MAAD) Model for optimized real-time data streaming. The key objective is to merge data streaming processes from various sources by optimizing and integrating them through a pipeline of multiple AI agents to enhance streaming efficiency, reliability, and scalability. Multiple AI agents increase resource utilization, control congestion, and optimize data routing to mitigate streaming failures. The MAAD model involves multiple AI agents: a load balancer, a resource finder and allocator, a fault detector, and an optimized streamer. A reinforcement learning model is implemented for predictive analytics and integrated with the AI agents to increase throughput and reduce latency. The experimental results show that the proposed MAAD model outperforms existing models in terms of fault tolerance, bandwidth efficiency, and continuous flow stability. The streaming dataset is taken from real-world streaming workloads for training and testing the AI models. The overall performance highlights that the MAAD model improves performance and is highly recommended for next-generation real-time data streaming applications.