This paper provides a comprehensive review of recurrent neural networks (RNNs), their architectures, variants, and applications. RNNs are designed to process sequential data by maintaining a hidden state that captures information about previous inputs. The paper discusses various RNN architectures, including long short-term memory (LSTM), gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), and peephole LSTM. It also covers recent innovations such as the integration of attention mechanisms and hybrid models combining RNNs with convolutional neural networks (CNNs) and transformer architectures. The study examines the application of RNNs in different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. The paper highlights the challenges and future directions of RNN research, emphasizing the need for further advancements in scalability, robustness, and interoperability. It also discusses the use of RNNs in various applications, such as text generation, machine translation, and time series prediction. The review aims to provide a comprehensive overview of the current state and future directions of RNN research, helping shape future research on neural networks.This paper provides a comprehensive review of recurrent neural networks (RNNs), their architectures, variants, and applications. RNNs are designed to process sequential data by maintaining a hidden state that captures information about previous inputs. The paper discusses various RNN architectures, including long short-term memory (LSTM), gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), and peephole LSTM. It also covers recent innovations such as the integration of attention mechanisms and hybrid models combining RNNs with convolutional neural networks (CNNs) and transformer architectures. The study examines the application of RNNs in different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. The paper highlights the challenges and future directions of RNN research, emphasizing the need for further advancements in scalability, robustness, and interoperability. It also discusses the use of RNNs in various applications, such as text generation, machine translation, and time series prediction. The review aims to provide a comprehensive overview of the current state and future directions of RNN research, helping shape future research on neural networks.