The rapid development of the Industrial Internet of Things (IIoT) has highlighted the critical need for efficient data transmission, security, and management. Recent advancements focus on integrating data compression, encryption, and error correction to optimize system performance while addressing resource constraints in IIoT devices. This paper reviews various innovative approaches to improve data transmission efficiency in IIoT systems, emphasizing the integration of machine learning, deep learning, and edgecloud computing. Techniques like projection-based coding, deep anomaly detection, and blockchain integration have demonstrated notable improvements in data compression, security, and system stability. Studies show that combining data compression with federated learning methods enhances data privacy, reduces communication overhead, and improves model accuracy. Additionally, novel compression methods, such as those based on Kronecker multiplication and attention mechanisms, have been shown to effectively manage large datasets, reduce latency, and conserve energy. Security remains a key concern, with studies exploring complex encryption algorithms and the role of image compression in enhancing data security without compromising system performance. Overall, this body of work underscores the importance of optimizing data flow, managing resources effectively, and developing secure, scalable solutions to address the challenges of modern IIoT applications.