Road pavement condition assessment is critical in transportation infrastructure management, as it helps ensure that maintenance budgets are allocated efficiently, allows for more informed prioritization of repair schedules, supports preventive maintenance strategies that extend pavement lifespan, identifies road segments at risk of structural failure based on surface distress indicators and environmental exposure, aids in reducing vehicle operating costs and improving road safety for the traveling public, fosters data-driven approaches to infrastructure monitoring, reduces misclassification of pavement conditions that could lead to deferred maintenance and costly emergency repairs, and increases the accuracy of predictive models used in asset management systems. In this module, we will analyze a pavement condition dataset by applying a Classical Gaussian Naive Bayes (GNB) classifier, a Hybrid Quantum Kernel Support Vector Machine, and a Variational Quantum Classifier (VQC), where we define parameters such as qubits, feature maps, and ansatz circuits and apply various packages and functions such as PennyLane, AngleEmbedding, StronglyEntanglingLayers, and NesterovMomentumOptimizer.