XTL-Net, a novel transfer learning framework, addresses this data scarcity by leveraging pre-trained models and fine-tuning them for rapid adaptation to new environments with minimal labelled data. This paper shows XTL-Net achieves ∼90% of baseline accuracy using only 10% of labelled data, while maintaining high real-time throughput (∼1000 inferences per second). By bridging the gap between high-performance deep learning and resource-efficient deployment, XTL-Net empowers organizations to harness real-time insights even in data-scarce scenarios. The capabilities enabled by high-performance, real-time data processing are driving transformative outcomes in multiple sectors, motivating the need for frameworks like XTL-Net. For instance, recent advances in remote patient monitoring have substantially improved healthcare delivery with 30% fewer medication errors, 20% fewer emergency admissions, and 14% fewer A&E visits reported due to continuous patient tracking via smart devices. XTL-Net contributes directly to making such high-impact, real-time systems viable across a wider range of resource-constrained or data-scarce domains.