Ball bearings are critical components of any industrial rotary equipment. They constitute about 90% of industrial machines’ components – and are thus responsible for the largest proportion of failures – approximately 70-85% of downtime. Defected bearings, while in service, give rise to high vibration amplitudes in rotary equipment, resulting in great reduction in their operational efficiency coupled with high energy consumption. Their premature and inadvertent failure could result in unplanned equipment downtown – thereby causing production loss and increased maintenance cost. Patently, to curtail this, it is vital that their health state is monitored throughout their service life for early faults detection, diagnosis, and prognosis. A knowledge of when a bearing will fail – that is, its remaining useful life (RUL) – can serve as supplement to maintenace decision-making such as determining in advance the time an equipment needs to be taken out-of-service and that can alternatively allow for sufficient lead time for maintenance planning as well. This can correspondingly result in enhancement in rotary systems effectiveness – i.e., availability, reliability, maintainability, and capability. Three popular condition monitoring approaches are signal processing-based approaches namely fault size estimation (FSE) and fault degradation estimation (FDE) as well as artifial intelligent (AI) based approach. It is, however, still a challenge to estimate a bearing fault size and therefore its RUL with high precision based on what has been diagnosed using these approaches. Accordingly, this review holistically explore capabilities and limitations of these approaches from recently published work. The reviewed limations are summarized and serve as new research avenue.