This project was undertaken to support the Michigan Department of Transportation’s (MDOT) implementation of a new Pavement Management Tool (PMT), a decision-support system designed to optimize pavement project selection. The goal was to develop data-driven inputs for the PMT using MDOT’s extensive historical pavement condition and maintenance data, with a focus on five key General Condition Ratings (GCRs): International Roughness Index (IRI), cracking (CRK), rutting (RUT), faulting (FLT), and the Pavement Distress Score (PDS). The project began with the compilation and preprocessing of condition data from MDOT’s GroupRecords files, which represent pavement lifecycle histories categorized by treatment type. Condition data were cleaned, harmonized, and filtered to remove post-treatment values and outliers, enabling the development of deterioration models using both discrete (threshold-based) and continuous (mathematical) approaches. Models were calibrated and fitted at the individual-section level, and times to Good/Fair and Fair/Poor thresholds were estimated. Two methods—percentile-based aggregation and group-level curve fitting—were compared to generate representative deterioration trends. In addition, condition improvements were quantified for various fix types by analyzing pre- and post-treatment data, supporting the development of action-benefit profiles. Utility scoring practices from other agencies were reviewed to guide recommendations for scaling and weighting GCRs within the PMT. The analysis also enabled the identification of typical treatment-trigger thresholds to inform network policy decisions. All modeling outputs, thresholds, and supplementary analyses are provided in a digital appendix, including tools to support further customization and application of the results in MDOT’s pavement management practice.