Natural Language Processing (NLP) has significant potential to improve peer review in scientific research. Peer review is a critical process for ensuring the quality of scientific work, but it is time-consuming, error-prone, and faces challenges such as scale, bias, and low-quality reviews. As large language models (LLMs) become more prevalent, there is growing interest in using NLP to assist with peer review. This paper explores how NLP can support peer review at various stages, from manuscript submission to final decision-making. It discusses the challenges and opportunities for NLP in peer review, including data collection, ethical considerations, and the need for standardized practices. The paper also highlights the importance of collaboration among researchers, policymakers, and funding bodies to advance NLP applications in peer review. It proposes a companion repository that aggregates datasets related to peer review and encourages contributions to this effort. The paper argues that while full automation of peer review may not be feasible, NLP can help address specific challenges within the peer review process, such as improving the quality of reviews, reducing bias, and increasing efficiency. The paper also emphasizes the need for careful consideration of ethical and privacy issues in NLP applications for peer review. Overall, the paper aims to provide a foundation for future research in NLP for peer review and to encourage the scientific community to explore new ways to improve the peer review process using NLP technologies.Natural Language Processing (NLP) has significant potential to improve peer review in scientific research. Peer review is a critical process for ensuring the quality of scientific work, but it is time-consuming, error-prone, and faces challenges such as scale, bias, and low-quality reviews. As large language models (LLMs) become more prevalent, there is growing interest in using NLP to assist with peer review. This paper explores how NLP can support peer review at various stages, from manuscript submission to final decision-making. It discusses the challenges and opportunities for NLP in peer review, including data collection, ethical considerations, and the need for standardized practices. The paper also highlights the importance of collaboration among researchers, policymakers, and funding bodies to advance NLP applications in peer review. It proposes a companion repository that aggregates datasets related to peer review and encourages contributions to this effort. The paper argues that while full automation of peer review may not be feasible, NLP can help address specific challenges within the peer review process, such as improving the quality of reviews, reducing bias, and increasing efficiency. The paper also emphasizes the need for careful consideration of ethical and privacy issues in NLP applications for peer review. Overall, the paper aims to provide a foundation for future research in NLP for peer review and to encourage the scientific community to explore new ways to improve the peer review process using NLP technologies.