Pavement condition monitoring represents a critical aspect of transportation infrastructure management, directly influencing road safety, lifecycle maintenance planning, and the overall efficiency of roadway networks. With the increasing demand for resilient and sustainable transportation systems, the development and implementation of effective pavement health assessment methods have become a top priority for both researchers and infrastructure authorities. In recent years, a variety of approaches have been proposed to evaluate road surface conditions. These include conventional manual surveys, sensors embedded within pavements, mobile sensing platforms installed on vehicles, crowdsourced data collection through smartphone applications, and remote sensing techniques such as aerial or satellite imagery. Each method offers distinct advantages and limitations when evaluated based on criteria such as cost, spatial and temporal resolution, data accuracy, operational complexity, automation potential, and scalability. Traditional approaches, although accurate and widely validated, are often constrained by high labor requirements, limited coverage, and elevated operational costs. In contrast, recent innovations involving low-cost inertial sensors mounted on passenger vehicles or smartphones provide a more affordable and scalable alternative for continuous, real-time pavement monitoring. However, these methods may face challenges regarding signal noise, environmental sensitivity, and the need for robust preprocessing algorithms to ensure reliable interpretation. Furthermore, the integration of aerial imaging and computer vision techniques has enabled the assessment of road surfaces in remote or difficult-to-access regions, thereby expanding the coverage of monitoring systems. The fusion of different sensing modalities (ground-based, vehicular, airborne, and crowdsourced) within a unified analytical framework holds the potential to create a comprehensive and intelligent monitoring system capable of predictive maintenance and timely decision-making. Such a hybrid and intelligent approach is particularly valuable for developing countries, where limited financial and technical resources necessitate cost-effective, scalable, and low-maintenance solutions. By leveraging real-time data streams and predictive analytics, transportation agencies can significantly enhance the reliability and efficiency of pavement management systems and move toward evidence-based infrastructure planning.