Quality management plays a crucial role in ensuring the success of construction projects by minimizing risks and enhancing stakeholder satisfaction. However, maintaining and improving quality in construction has always been a significant challenge, especially as technical requirements and standards become increasingly demanding. Therefore, the adoption of new technologies such as Building Information Modeling (BIM) and Artificial Intelligence (AI) has emerged as an effective solution to improve quality management. This study aims to assess the role of BIM and AI in enhancing the quality of construction projects by reducing errors and optimizing costs and time. The research methodology involves collecting and analyzing data from real construction projects and comparing quality indicators between projects that use and do not use BIM and AI. Key metrics such as error rates, rework time and costs, and customer satisfaction levels are evaluated in detail to determine the effectiveness of these technologies. The results indicate that the application of BIM and AI reduces the technical error rate from 15% to 5% and decreases the number of reworks by up to 40%, providing significant benefits in terms of cost and construction schedule. Additionally, stakeholder satisfaction has notably improved, reaching up to 85%, thanks to the transparency and effective control provided by these systems. The findings of this study systematically contribute to building a theoretical and practical framework for quality management in the construction industry while also proposing broader applications of BIM and AI in the future.