It’s a capacity to bridge the gap between human language processing NLP has emerged as one of the most significant fields of artifical intelligence research. By allowing models to directly learn intricate linguistic patterns from data, contemporary deep learning techniques have been instrumental in recent years in enhancing the performance of NLP systems. Fundamental component of artificial intelligence, natural language processing NLP allows machines to efficiently comprehend, evaluate, and produce human language. As deep learning has grown quickly, sophisticated neural architectures that can recognize intricate linguistic patterns have greatly improved conventional NLP techniques. An analytical review of contemporary deep learning methods used in natural language processing is presented in this paper. They analysis highlights the working of concept, advantages, and disadvantages of popular models like Transformer based architectures, Long Short Term Memory LSTM networks, and Recurrent Neural Networks RNNs . Describing the fundamentals, advantages, and disadvantages of Transformer based architectures and Long Short Term Memory LSTM networks. illustrate the usefulness of these methods, a number of NLP applications are examined, such as information extraction, machine translation, sentiment analysis, and text classification. To assess performance patterns, scalability, and contextual awareness among various models, a comparative analysis is presented. Current issues with deep learning–based NLP systems, including data dependency, computational complexity, and interpretability problems, are also discussed in the survey. This paper attempts to provide researchers and students with a clear understanding of the development of deep learning in NLP by combining recent research findings. It also aims to identify possible future research directions in this rapidly developing field. An analytical review of contemporary deep learning methods for natural language processing is presented in this paper along with an analysis of how these methods have changed conventional language processing techniques. The paper explain the working principles of various deep learning techniques and highlights how advancements such as attention mechanism and transformer models have significantly improved contextual understanding while reducing the limitations observed in earlier sequential models. The evolution and significance of deep learning architectures in natural language processing are examined in this analytical overview, which focuses on training methodologies, model architectures, and representation learning. Distributed word representations and sequential modelling were made possible by early neural techniques like feedforward and recurrent neural networks. The survey is starts off by going over early neural techniques that can allowed machines to recognise syntactic and semantic linkages in the text, such as a word embedding and recurrent neural networks. After that, it looks at the shortcomings of sequential models and how attention mechanisms were later introduced to enhance contextual awareness. The survey also identifies important issues with model interpretability, computing efficiency, scalability, and ethical implications. This work gives system overview of the contemporary deep learning methods in the natural language processing through the methodology evolution can recent develop. The advent of contemporary deep learning methods has significantly changed natural language processing, or NLP. The development, approaches, and effects of deep learning models in NLP applications are examined in this analytical survey. It looks at fundamental designs including convolutional neural networks CNNs , long short term memory LSTM networks, and recurrent neural networks RNNs , emphasizing their contributions to feature extraction and sequence modelling. The study also examines the emergence of transformer based models and attention processes, especially Attention Is All You Need, which transformed language representation learning. Performance, scalability, and practical uses like machine translation, sentiment analysis, and question answering are assessed for sophisticated pre trained language models like BERT and GPT. Through a comparison of advantages, disadvantages, and computational difficulties, this survey offers thorough insights on current. Processing Natural Languages, Deep learning, Network of neural machines, Learning machines, Models of transformers, Neural network, Neural network with convolution, Modeling machines, Classification of text, Natural language processing NLP , Multimodal learning, Encoder Decoder Architecture, Large Language Models, Representation Learning, Parameter Efficiency, RoBERTa, Fine Tuning Strategies, Few Shot Learning, Scalability, Gated Recurrent Units GRU , Interpretability, Sentiment Analysis. 45-53 Advances in Computer Applications and Information Technology Special Issue Sharvari Tikhat | Aastha Shahakar