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Natural Language Processing (NLP) Has Witnessed Rapid Advancements In Recent Years, Largely Driven By The Integration Of Deep Learning Techniques. This Survey Explores The Diverse Applications Of Deep Learning In NLP, Highlighting Key Models, Architectures, And Methodologies That Have Transformed The Field. Beginning With Foundational Neural Approaches Such As Word Embeddings And Recurrent Neural Networks, The Survey Traces The Evolution Toward More Advanced Architectures, Including Convolutional Neural Networks, Sequence-to-sequence Models, Attention Mechanisms, And Transformer-based Frameworks. The Applications Span A Wide Range Of Tasks, Such As Machine Translation, Sentiment Analysis, Question Answering, Text Summarization, Speech Recognition, And Dialogue Systems. Moreover, We Examine The Advantages Of Deep Learning In Capturing Semantic Meaning, Context, And Linguistic Nuances Compared To Traditional Statistical Methods. Challenges Such As Interpretability, Data Requirements, Computational Costs, And Ethical Concerns Are Also Discussed. This Survey Provides An Overview Of The Current Landscape Of Deep Learning In NLP While Pointing To Promising Directions For Future Research And Applications.