In the rapidly evolving field of machine learning, the shift from batch-processed to real-time data processing is becoming essential, particularly in time-sensitive industries like food delivery. This transformation is facilitated by streaming data engines such as Redpanda, which, through technologies like WebAssembly, enable real-time data transformation and prediction in machine learning applications. The traditional batch-processing approach in food delivery prediction models often leads to outdated insights due to the latency between data collection and analysis. By leveraging Redpanda, data can be ingested, transformed, and fed directly into machine learning models built with TensorFlow for immediate prediction updates, thereby reducing latency and improving model accuracy. This approach not only simplifies the architecture by eliminating additional data-processing layers but also allows for dynamic model updates as new data continuously flows in. While demonstrated in the context of food delivery, the application of Redpanda's real-time data processing capabilities holds potential across various sectors requiring immediate data insights, such as finance, healthcare, and smart city management, positioning Redpanda as a pivotal player in the future of real-time machine learning applications.