Abstract. Menstrual cycle disorders are common reproductive health problems that may develop as a result of endocrine, metabolic, gynecological, and functional abnormalities. Early identification of menstrual disturbances is important for preventing reproductive complications and determining the underlying causes. This study aimed to develop and improve a structured screening algorithm for the early detection of menstrual cycle disorders. The proposed approach included assessment of menstrual history, clinical manifestations, risk factors, endocrine and metabolic status, as well as appropriate laboratory and instrumental investigations. The findings demonstrated that menstrual irregularity was associated with several potential risk factors, including increased body weight, polycystic ovary syndrome-related changes, thyroid dysfunction, hyperprolactinemia, metabolic abnormalities, and psychological stress. A three-stage screening algorithm was developed, consisting of initial clinical assessment, risk-factor stratification, and targeted laboratory and instrumental examination.