Due to the presence of natural noise in user data recommendation systems tend to give inaccurate or irrelevant recommendations. This is a problem addressed in this research by designing improved machine learning models that extend to filter natural noise. The presented approach combines supervised and unsupervised learning models which help to distinguish real user preferences from noise effectively. Among them are adaptive filter elements of the given system that can adjust the filter coefficient to the level of noise and a hierarchical-structured classification model that leads to multiple hierarchical levels of filtering of the user data. In order to test the efficiency and accuracy of the proposed algorithms, several experiments were performed on actual datasets available. These outcomes indicate that optimization with noise reduction increases the system’s recommendation precision and recall levels by 15–20% higher than baseline models. Moreover, the ability of the system to work with various types of noise is also promising if the user data are noisy by their nature in the given domain. This research addresses the major challenges of recommendation systems by presenting a reliable solution for natural noise treatment and thus improving the quality of recommendations provided to the users.