In the realm of Internet of Things (IoT) healthcare, the efficient processing of multimedia data poses significant challenges due to the diversity and volume of information generated. This paper presents a novel Deep Learning-based Hybrid Framework (DLHF) designed to address these challenges by integrating advanced deep learning techniques with IoT infrastructure. The framework leverages convolutional neural networks (CNNs) for image and video analysis, recurrent neural networks (RNNs) for time-series data processing, and attention mechanisms for enhancing model interpretability and performance. Furthermore, DLHF incorporates edge computing capabilities to optimize resource utilization and reduce latency, crucial for real-time healthcare applications. The effectiveness of DLHF is demonstrated through experimental evaluations using a dataset of multimedia healthcare data, highlighting its superior performance compared to traditional methods. This framework not only enhances the accuracy and efficiency of multimedia data analysis in IoT healthcare but also establishes a foundation for future developments in personalized and responsive healthcare systems.