Bovine respiratory diseases (BRD) represent a substantial challenge to the worldwide cattle industry, resulting in considerable economic losses and diminished animal well-being. Timely detection of BRD is imperative for prompt intervention and effective disease control. This chapter delves into the utilization of deep learning methodologies to achieve early detection of respiratory ailments in cattle. Through a thorough examination of the available techniques, we propose an innovative deep-learning framework tailored for this purpose. Furthermore, we offer experimental findings that underscore the efficiency of our proposed system. By leveraging advanced computational techniques, such as convolutional neural networks (CNNs) and transfer learning, our approach aims to revolutionize the early diagnosis of BRD, thereby facilitating improved animal health outcomes and enhanced productivity in the cattle industry.