Chronic heart failure is a significant comorbidity in patients requiring prolonged noninvasive ventilation (NIV), yet accurate prediction of mortality risk in this cohort remains challenging. Existing scoring systems and machine learning (ML) models developed for predicting NIV failure and mortality face limitations when applied exclusively to chronic heart failure patients on prolonged NIV. This chapter reviews the current literature on prognostic factors, scoring systems, and ML models related to prolonged NIV use and mortality prediction in those patients. While general scores like SOFA, HACOR, and NIVO provide some utility in predicting NIV failure and mortality, they lack specificity for chronic heart failure patients requiring prolonged NIV. Machine learning models hold promise by integrating a diverse set of variables, but most of them do not discriminate between acute and prolonged NIV use or between invasive and noninvasive ventilation. Given the distinct implications of invasive and noninvasive ventilation, the creation of specific predictive models for each ventilation type could further improve patient management strategies. To enhance risk prediction in chronic heart failure patients on prolonged NIV, future studies should aim to develop and validate prediction models using variables specific to this population, such as cardiac biomarkers, duration of NIV use, adherence to NIV, and underlying respiratory disorders like COPD or sleep-disordered breathing.