The advent of 6G and Industry 4.0 technologies has revolutionized industrial automation, connectivity, and data processing. With the growing complexity of heterogeneous data environments in these domains, detecting faults in real-time has become increasingly challenging. This paper proposes a sustainable deep learning framework that integrates advanced neural networks with resource-efficient processing techniques for fault detection in 6G-enabled Industry 4.0 environments. The framework leverages data from various sources, including IoT devices, sensors, and industrial machines, ensuring high accuracy, scalability, and energy efficiency. A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is employed to capture both spatial and temporal data patterns. The framework is designed to optimize resource allocation while maintaining fault detection performance. Simulation results demonstrate the efficacy of the proposed approach, highlighting its potential to enhance fault management in smart industrial systems.