Successful pavement management plays a significant role in providing safe, long-lasting, and cost-effective road infrastructure. This study investigates the Pavement Management System (PMS) and the Pavement Maintenance Management System (PMMS) as core models to improve pavement performance and extend service life. A discussion of pavement condition assessment methodologies is presented in this paper, with emphasis on the key parameters influencing pavement performance, such as ambient conditions, traffic loading, material properties, and maintenance procedures. To enhance the pavement condition assessment process and avoid dependence on a singular approach, the study considered three significant pavement condition rating approaches: manual evaluations, relying on visual observation by qualified raters; empirical approaches, using statistical relationships based on previous experiences to predict degradation trends; and automatic systems, using sensor cars and half-automatic technologies to offer efficient, deflection-based estimates. Also considered is the convergence of different approaches with a special emphasis on nascent innovations such as artificial intelligence (AI) and real-time data capture for improved accuracy of pavement assessment and decision-making. The outcomes indicate the importance of a multi-method pavement evaluation considering cost, accuracy, and scalability. Future research should focus on improving data integration, predictive model refinement, and the use of smart infrastructure technologies to improve pavement management efficiency and sustainability.