This chapter concludes this book on urban railway alignment optimization. Set against deepening urbanization and infrastructure development, the book presents a research framework integrating data-driven, intelligent optimization, and human–machine collaboration concepts. It explores urban railway alignment optimization through five sections: theoretical foundations, system modeling, algorithm implementation, platform integration, and engineering validation. Key research includes clarifying refined railway alignment design concepts, modeling urban railway alignment system constraints using reliability theory and BNN, designing a reinforcement learning-based methodology, proposing the BA-FORA model, and developing a CAD plugin for intelligent alignment optimization. The findings provide a theoretical foundation and technical approach for urban rail transit development. The chapter also offers policies and recommendations from four perspectives and prospects for future research.