During the last decade, rainfall monitoring using signal level data from commercial microwave links (CMLs) in cellular communication networks has been proposed as a complementary way to traditional methods. This approach has shown promising results in various studies, but its accuracy and reliability are still being evaluated. The proposed algorithm aims to improve the accuracy of rainfall retrieval from CMLs by incorporating machine learning techniques and advanced signal processing methods. The algorithm will be tested and validated using a large dataset of rainfall measurements and CML signal levels. The results will be compared with traditional methods to assess the performance of the proposed algorithm. The study will provide valuable insights into the potential of CMLs for rainfall monitoring and will contribute to the development of more accurate and reliable methods for rainfall estimation.