Predicting the performance and remaining service life of deteriorated reinforced concrete (RC) structures based on observational information obtained through methods such as visual inspection and monitoring remains a significant challenge within the structural engineering community. Experimental studies on the process of steel corrosion and its subsequent impact on the structural performance and integrity of deteriorated RC structures have demonstrated the need for improved numerical simulation techniques to accurately replicate observed phenomena. Addressing this complex task requires the integration of various advanced technologies and a proper account of the uncertainties involved in estimating and evaluating each stage of structural deterioration and its progression in both space and time. This paper provides a state-of-the-art review of the methods and technologies applied in the probabilistic remaining service life assessment of corroded concrete structures. These include: (a) corrosion detection methods, (b) experimental investigation and finite element (FE) analysis of the structural performance of corroded concrete components, (c) probabilistic time-dependent reliability assessment using stochastic simulation methods, and (d) Bayesian updating and machine learning techniques. This review aims to highlight the current status and obstacles of these methods and technologies in assessing the remaining service life of corrosion-affected concrete structures.