MDOT has been using the Distress Index (DI) since the inception of its pavement management system (PMS) in the early 1990s. DI was developed to help MDOT engineers decide, allocate budgets, and prioritize future maintenance or reconstruction activities. However, the raw data requirements for the DI are complicated (and somewhat unique compared to the rest of the nation). Over the last three decades, the pavement industry has seen many advances in data collection, distress identification, performance modeling, and other processes fundamental to PMSs. Consequently, there was a need to revisit the DI used by MDOT and revise it according to modern pavement data collection standards and calculation methodology. This study aimed to develop an enhanced pavement condition score and associated PMS data collection methodology for use by MDOT. To meet this objective, 2081 flexible and 741 rigid pavement sections were selected from MDOT’s performance database. Then, five different condition indices used by other state agencies were computed using the MDOT's PMS data and compared against MDOT’s Distress Index (DI). Maintenance records were used to compare the magnitudes of different indices right before maintenance activities were performed. The new pavement condition parameter was selected to follow the current state of the practice in its rating scale and consider major distresses. Furthermore, various performance models were used to predict the new condition index and International Roughness Index (IRI) data, and pavement fix lives were estimated for both asphalt and rigid pavements. Building on these advancements, network-level modeling methods were developed to project the future condition of MDOT’s pavement network in terms of IRI, cracking, rutting, and faulting. Using Markovian Transition Probability Matrices (TPMs) and multinomial logistic regression, the study established a robust analytical framework to forecast pavement performance under various maintenance and rehabilitation scenarios. These models enable MDOT to evaluate the long-term effects of different funding strategies, set realistic performance targets in alignment with federal requirements, and support data-driven decision-making for statewide pavement management.