The effective management and maintenance of civil infrastructure, particularly in buildings, are vital for ensuring structural integrity and safety. This paper investigates the application of Artificial Intelligence (AI) in enhancing precision crack detection and maintenance of building infrastructure. Traditional methods of crack detection often rely on manual inspections, which can be time-consuming, labor-intensive, and prone to human error. AI technologies, particularly machine learning and computer vision, offer significant advancements in automating and improving the accuracy of crack detection. By employing AI-driven systems, real-time data from various sensors and imaging devices can be analyzed to identify and classify cracks with high precision. These systems can detect minute cracks that might be overlooked by human inspectors, thus preventing minor issues from escalating into major structural problems. The integration of AI with other modern tools such as drones and robotic inspection devices further enhances the reach and effectiveness of monitoring efforts. Moreover, AI can assist in predictive maintenance by analyzing patterns and trends from historical and real-time data, enabling proactive maintenance strategies. This approach not only enhances the safety and durability of buildings but also reduces maintenance costs and downtime. The paper also discusses the challenges of implementing AI in crack detection, including the need for large datasets for training algorithms, the complexity of integrating AI with existing infrastructure, and addressing data privacy concerns. In conclusion, leveraging AI for crack detection and maintenance in building infrastructure represents a significant step forward in ensuring structural safety and operational efficiency. This paper provides insights into the current state of AI applications in this field and offers recommendations for future research and development.