Distributed deep learning enables the training of complex neural networks across multiple computing nodes, effectively handling large datasets and reducing training time. By leveraging parallelism at data and model levels, distributed systems overcome the limitations of single-machine resources, enabling scalability and efficiency. Key techniques include data parallelism, model parallelism, and hybrid approaches, often supported by communication frameworks like parameter servers and all-reduce algorithms. Distributed deep learning is critical for advancing applications in natural language processing, computer vision, and large-scale recommendation systems. Challenges such as synchronization overhead, fault tolerance, and resource heterogeneity continue to drive research toward more robust and efficient distributed training frameworks.