Accurate prediction of pavement performance and remaining lifespan is a critical element of any pavement management system. Many entities currently use deterministic pavement performance prediction models, which may not fully capture the inherent uncertainties in pavement deterioration. This research aims to develop a probabilistic-based model to predict pavement performance and remaining service life for pavements in Germany. Two datasets from the German Federal Highway Research Institute (BASt), focusing on SMA (stone mastic asphalt) and MA (mastic asphalt) are used. The data set consists of hundreds of sections (hectometers) where surface condition measurements were taken approximately every six months (autumn and spring) from 2011 to 2021. These measurements cover three pavement distresses: i) rutting, ii) longitudinal evenness, and iii) cracking. In addition to the condition data pertaining to the roads, the construction data, including the asphalt mix used and the year of construction, are also described. This study applies a combination of the current deterministic approach employed in Germany and a probabilistic Markov-based model. After converting the physical measured variables into dimensionless condition grades (from 1: very good, to 5: bad) the deterministic approach uses functions dedicated to rating the pavement condition under these distress types and ranges from 1 to 4 describing the change in condition (with 1 indicating a slow, and 4 indicating a very fast deterioration of condition). The methodology presented herein involves forecasting the dimensionless condition grades to characterize the change in pavement deterioration using a discrete-time Markov process through transition matrices for each pavement type and distress. This approach allows for the calculation of the condition rating as a function of pavement age. By integrating these methods, the research aims to improve the accuracy of estimating pavement condition and remaining service life, thereby enabling more efficient maintenance planning and advancing the concept of predictive infrastructure management.