This paper provides a comprehensive review of deep learning methods and models used in Natural Language Processing (NLP). It highlights the evolution of deep learning in NLP, from early shallow models to the current state-of-the-art techniques. The authors discuss various deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their applications in tasks like sentiment analysis, question answering, and named entity recognition. They also explore memory-augmenting strategies, attention mechanisms, and the integration of unsupervised models and reinforcement learning. The paper emphasizes the importance of word embeddings, particularly Word2Vec, and their role in capturing semantic and syntactic information. Additionally, it covers character embeddings and contextualized word embeddings like ELMo and BERT, which provide deeper representations by leveraging context. The paper concludes with a detailed analysis of CNNs and RNNs, including their architectural details and performance in different NLP tasks. Overall, the review aims to provide a comprehensive understanding of the past, present, and future of deep learning in NLP.This paper provides a comprehensive review of deep learning methods and models used in Natural Language Processing (NLP). It highlights the evolution of deep learning in NLP, from early shallow models to the current state-of-the-art techniques. The authors discuss various deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their applications in tasks like sentiment analysis, question answering, and named entity recognition. They also explore memory-augmenting strategies, attention mechanisms, and the integration of unsupervised models and reinforcement learning. The paper emphasizes the importance of word embeddings, particularly Word2Vec, and their role in capturing semantic and syntactic information. Additionally, it covers character embeddings and contextualized word embeddings like ELMo and BERT, which provide deeper representations by leveraging context. The paper concludes with a detailed analysis of CNNs and RNNs, including their architectural details and performance in different NLP tasks. Overall, the review aims to provide a comprehensive understanding of the past, present, and future of deep learning in NLP.