The advancement of Artificial Intelligence (AI) has transformed civil engineering, enabling innovative approaches to complex decision-making processes. This research presents an AI-enhanced contractor evaluation framework designed to optimize selection processes in civil engineering projects. Built on the principles of the Analytical Hierarchy Process (AHP), the model ensures systematic, objective, and transparent decision-making. The evaluation focuses on key criteria, including cost, annual turnover, labor availability, prior experience, equipment resources, and project completion timelines, with weights assigned using AHP. AI integration significantly enhances the traditional contractor evaluation process, incorporating machine learning models to predict contractor reliability, clustering algorithms to group contractors based on similar performance characteristics, and a rule-based logic system for risk assessment. Normalization techniques standardize contractor scores, enabling equitable comparisons for informed decision-making. Interactive visualization tools provide stakeholders with a clear, data-driven understanding of contractor performance and risk levels, facilitating real-time exploration of results. This case study on real-world contractor data demonstrates the framework’s effectiveness in automating evaluations, reducing human bias, and improving decision quality. By combining AHP with advanced AI-driven techniques, the proposed solution is scalable and adaptable to various project types, offering a comprehensive tool for efficient resource management and risk mitigation.