The peer review process is essential for academic research, yet it faces challenges such as inefficiencies, biases, and limited access to qualified reviewers. This paper introduces an autonomous peer reviewer selection system that employs the Natural Language Processing (NLP) model to match submitted papers with expert reviewers independently of traditional journals and conferences. Our model performs competitively in comparison with the transformer-based state-of-the-art models while being 10 times faster at inference and 7 times smaller, which makes our platform highly scalable. Additionally, with our paper-reviewer matching model being trained on scientific papers from various academic fields, our system allows scholars from different backgrounds to benefit from this automation.