Cities serve as engines of economic growth and innovation, yet sustaining them requires strategies to address climate change, resource scarcity, and social inequity. While urban planning traditionally relied on deterministic models, today's interconnected challenges demand sophisticated, AI-augmented approaches. Machine learning (ML) empowers planners to manage and mitigate these complexities by leveraging massive datasets for real-time predictions across infrastructure, mobility, and social systems. This edited volume examines how ML supports sustainable smart cities and urban environments that prioritize resident quality of life while maintaining economic resilience. It explores ML-driven innovations in building design, optimized urban mobility, and critical resilience efforts like flood management and air quality monitoring. Furthermore, it highlights tools for social equity and community well-being. Featuring contributions from global experts in architecture, engineering, and computer science, this collection offers actionable insights for building sustainable urban futures by bridging theoretical advancements with practical case studies.

Thomas Walker is Professor in the Department of Finance at the John Molson School of Business, Director of the Jacques Ménard/BMO Center for Capital Markets at Concordia University, Canada, and Executive Director of the Institute for Sustainable Finance.

Liangzhu Leon Wang is Professor in the Department of Building, Civil and Environmental Engineering and Associate Director of the Center for Zero Energy Building Studies at Concordia University, Canada.

Sherif Goubran is Assistant Professor in the Department of Architecture in the School of Sciences and Engineering at The American University in Cairo, Egypt.

Ahmed Marey is a PhD candidate in the Department of Building, Civil, and Environmental Engineering in the Gina Cody School of Engineering and Computer Science at Concordia University, Canada.