Hearing loss is a growing public health issue, aggravated by increasing exposure to high-intensity environmental and recreational noise. Conventional audiometric testing relies heavily on manual procedures performed by audiologists, which are time-consuming and prone to errors. The aim of this paper is to introduce Sonalyze, a Python-based application for automated generation and basic interpretation of audiograms. The system implements pure-tone generation, patient response capture with dual validation, automatic audiogram plotting, and rule-based pattern recognition for common pathologies such as noise-induced hearing loss and presbycusis. Validation on synthetic datasets demonstrated compliance with audiometric standards and correct identification of characteristic patterns, supporting the technical feasibility of the solution. Sonalyze has the potential to increase efficiency, reproducibility, and accessibility of audiological screening, with future prospects for clinical validation and integration in tele-audiology services.