Knee osteoarthritis (KOA) is a leading cause of disability, primarily driven by the progressive degradation of articular cartilage (AC) resulting from mechanical overuse during daily activities. Accurately predicting cartilage failure remains a major challenge due to the complex interplay of biomechanical, demographic, and clinical factors. This chapter presents a hybrid framework that combines a validated mathematical degradation model with machine learning (ML) techniques to estimate the remaining mechanical cycles before cartilage failure. The approach involves simulating a range of degradation scenarios using the mathematical model to generate a synthetic dataset incorporating key biomechanical and demographic inputs. Four ML algorithms are then trained and evaluated on this dataset. Among them, the Support Vector Regressor (SVR) demonstrates the highest predictive performance, achieving R2 = 0.95, RMSE = 0.13, and MAPE = 2.5%. This integrated modeling strategy represents a promising step toward early KOA risk stratification and the development of personalized, data-driven preventive interventions.