The rapid proliferation of 5G networks and Internet of Things (IoT) technology has created new opportunities and challenges in the quest for enhanced latency performance. This research paper explores the implementation of deep neural networks (DNNs) and convolutional neural networks (CNNs) in 5G-enabled IoT systems to address the latency challenge. By leveraging the power of DNNs, CNNs, and 5G networks, our study aims to significantly reduce latency and improve response times within IoT environments. Through a systematic methodology, including data collection, model architecture design, training, integration with 5G infrastructure, and performance evaluation, we analyze the effectiveness of these advanced machine learning techniques. Our implementation work includes real-world use cases from diverse IoT domains, such as industrial automation, healthcare monitoring, and smart city infrastructure. This paper assess the impact of our implementation on latency reduction, response times, and overall system performance. The findings of this research provide valuable insights into the feasibility and benefits of integrating DNNs and CNNs in 5G-enabled IoT for enhanced latency, paving the way for improved IoT applications and services in latency-sensitive domains.