Bridges are essential to transportation systems, yet thousands in the United States are aging and require continuous monitoring to remain safe. As inspection demands grow, artificial intelligence is emerging as a potential tool for identifying structural risks earlier and improving maintenance planning. This project will investigate how artificial intelligence and predictive data analysis could improve maintenance strategies for bridge infrastructure. The study will focus on whether machine learning and large infrastructure datasets can help identify deterioration patterns and potential structural issues before they become critical. Using data from infrastructure databases such as the National Bridge Inventory, I will examine trends in bridge condition ratings, deterioration patterns, and maintenance history. Additional insight will be gained through an internship with an administrative engineer at the New York City Department of Transportation, where I will observe infrastructure inspection practices and learn how bridge data is collected and used in real-world maintenance decisions. Through this research, I expect to develop a framework for analyzing bridge condition data and evaluating how AI-based models could detect patterns associated with structural deterioration. The analysis is expected to demonstrate how predictive systems may help engineers identify maintenance needs earlier and prioritize repairs more effectively. If successful, the research could demonstrate how combining traditional inspection methods with predictive data analysis may improve bridge safety and help transportation agencies allocate maintenance resources more efficiently.