Artificial Intelligence (AI) is revolutionizing structural engineering by improving design accuracy, efficiency, and automation. This study specifically investigates the role of AI in the design and drafting of Reinforced Cement Concrete (RCC) beams, in accordance with IS 456:2000 standards. The research focuses on four distinct types of RCC beams: simply supported beams, doubly reinforced beams, cantilever beams, and continuous beams, utilizing Python programming for automating computations, dataset handling, and model integration. To ensure precise structural calculations, the project creates structured datasets incorporating design parameters such as span length, load, and reinforcement percentage, helping to reduce human errors and improve the reliability of the design process. By incorporating AI-driven algorithms, the design process is streamlined, allowing for the rapid generation of accurate beam designs that comply with industry standards. A key aspect of the project is the development of a web-based interface using HTML and Python. This interface serves as an easy-to-use platform where users can input the necessary design parameters for RCC beams. After the data is entered, the system generates optimized designs that are fully compliant with IS 456:2000 and provides downloadable, detailed design drawings. This approach reduces manual intervention and improves the speed of design output. The integration of AI in this process achieves precision within a 2% variance compared to conventional manual methods and facilitates the creation of cost-effective and durable structural solutions. This project highlights the impact of AI in enhancing automation and accuracy in structural engineering and sets the stage for its future application in designing other critical structural elements such as columns, slabs, and entire buildings. Looking to the future, the research anticipates further advancements in AI integration, particularly through potential collaborations with specialized structural analysis software like STAAD. Such integration would expand the system's capabilities, enabling more advanced automation, accuracy, and functionality in structural analysis and design.