In this study, we explored the potential of seventeen quantitative ultrasound parameters (radiofrequency-based) in assessing the progressive loss of collagen and proteoglycans (mimicking an osteoarthritis condition) in ex-vivo bovine cartilage samples. The majority of the analyzed metrics showed significant changes as the degradation progressed due to trypsin and collagenase treatment. For the first time, we employed a combination of these ultrasound parameters to create machine learning models for the automated detection of a model of healthy and degraded cartilage samples. A logistic regression model exhibited a remarkable capability of distinguishing between healthy and collagenase-treated cartilage, achieving accuracy and an area under the curve values of 93% and 90%, respectively. When comparing healthy and trypsin-treated cartilage, an ensemble model yielded accuracy and an area under the curve values of 83% and 75%, respectively. Histological and mechanical analyses further confirmed the ultrasound findings, as collagenase had more pronounced impact on both mechanical and histological properties compared to trypsin. These metrics were obtained using an ultrasound probe, with a transmission frequency of 15 MHz, typically used for the diagnosis of musculoskeletal diseases. As a perspective, the proposed quantitative ultrasound assessment could become a new standard for monitoring cartilage health, aiding in the early detection of cartilage pathologies and enabling prompt interventions.