Deep Learning has significantly transformed Natural Language Processing (NLP) by enabling machines to understand, interpret, and generate human language with remarkable accuracy. Inspired by the structure of the human brain, deep neural networks learn complex patterns from large volumes of unstructured data through multiple nonlinear layers. This paper presents an overview of deep learning techniques used in NLP, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTM and GRU architectures, Autoencoders, Sequence-to-Sequence models, and Transformers. It also highlights the historical evolution of deep learning from early neural models such as the McCulloch-Pitts neuron and Perceptron to modern breakthroughs like AlexNet and Transformer architectures. The paper further explores the importance and real-world applications of NLP across domains such as healthcare, sentiment analysis, machine translation, chatbots, content recommendation, and information retrieval. In addition, it discusses major challenges in deep learning, including overfitting, data limitations, computational costs, interpretability, scalability, bias, and adversarial attacks, along with strategies to address these issues. Overall, deep learning continues to drive innovation in NLP, offering powerful solutions while requiring responsible and efficient implementation practices.